Zy Filemanager

📁 File Browser

Name	Size	Permissions	Actions
📄 __init__.cpython-313.opt-1.pyc (155 bytes)	155 B	0644	✍️ ⬇️ 🗑️
📄 __init__.cpython-313.opt-2.pyc (155 bytes)	155 B	0644	✍️ ⬇️ 🗑️
📄 __init__.cpython-313.pyc (155 bytes)	155 B	0644	✍️ ⬇️ 🗑️
📄 module_docs.cpython-313.opt-1.pyc (19,839 bytes)	19.37 KB	0644	✍️ ⬇️ 🗑️
📄 module_docs.cpython-313.opt-2.pyc (19,839 bytes)	19.37 KB	0644	✍️ ⬇️ 🗑️
📄 module_docs.cpython-313.pyc (19,839 bytes)	19.37 KB	0644	✍️ ⬇️ 🗑️
📄 topics.cpython-313.opt-1.pyc (535,704 bytes)	523.15 KB	0644	✍️ ⬇️ 🗑️
📄 topics.cpython-313.opt-2.pyc (535,704 bytes)	523.15 KB	0644	✍️ ⬇️ 🗑️
📄 topics.cpython-313.pyc (535,704 bytes)	523.15 KB	0644	✍️ ⬇️ 🗑️

📤 Upload Files

Single File

Multiple Files

ZIP Extract

ZIP file will be deleted after extraction

➕ Create New

New Folder

New File

✍️ Editing: topics.cpython-313.pyc

            File: /opt/alt/python313/lib64/python3.13/pydoc_data/__pycache__/topics.cpython-313.pyc

            Size: 523.15 KB | 
            Last Modified: 2026-04-28 18:32:07        

�

����9��ik*���������������������������0�S�S_SS_SS_SS_SS	_S
S_SS
_SS_SS_SS_SS_SS_SS_SS_SS_SS_S S!_0�S"S#_S$S%_S&S'_S(S)_S*S+_S,S-_S.S/_S0S1_S2S3_S4S5_S6S7_S8S9_S:S;_S<S=_S>S?_S@SA_SBSC_E0�SDSE_SFSG_SHSI_SJSK_SLSM_SNSO_SPSQ_SRSS_STS?_SUSV_SWSX_SYSZ_S[S\_S]S^_S_S`_SaSb_ScSd_E0�SeSf_SgSh_SiSj_SkSl_SmSn_SoSp_SqSr_SsSt_SuSv_SwSx_SySz_S{S|_S}S~_SS�_S�S�_S�S�_S�S�_ES�S�S�S�S�S�S�S�S�S�S�S�S�.Er�g�)��assertav��The "assert" statement
**********************

Assert statements are a convenient way to insert debugging assertions
into a program:

assert_stmt ::= "assert" expression ["," expression]

The simple form, "assert expression", is equivalent to

if __debug__:
       if not expression: raise AssertionError

The extended form, "assert expression1, expression2", is equivalent to

if __debug__:
       if not expression1: raise AssertionError(expression2)

These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names.  In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option "-O").  The current code generator emits no code for an
"assert" statement when optimization is requested at compile time.
Note that it is unnecessary to include the source code for the
expression that failed in the error message; it will be displayed as
part of the stack trace.

Assignments to "__debug__" are illegal.  The value for the built-in
variable is determined when the interpreter starts.
�
assignmentu�,��Assignment statements
*********************

Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:

(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)

An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.

Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable.  The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).

Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.

* If the target list is a single target with no trailing comma,
  optionally in parentheses, the object is assigned to that target.

* Else:

* If the target list contains one target prefixed with an asterisk,
    called a “starred” target: The object must be an iterable with at
    least as many items as there are targets in the target list, minus
    one.  The first items of the iterable are assigned, from left to
    right, to the targets before the starred target.  The final items
    of the iterable are assigned to the targets after the starred
    target.  A list of the remaining items in the iterable is then
    assigned to the starred target (the list can be empty).

* Else: The object must be an iterable with the same number of items
    as there are targets in the target list, and the items are
    assigned, from left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as
follows.

* If the target is an identifier (name):

* If the name does not occur in a "global" or "nonlocal" statement
    in the current code block: the name is bound to the object in the
    current local namespace.

* Otherwise: the name is bound to the object in the global namespace
    or the outer namespace determined by "nonlocal", respectively.

The name is rebound if it was already bound.  This may cause the
  reference count for the object previously bound to the name to reach
  zero, causing the object to be deallocated and its destructor (if it
  has one) to be called.

* If the target is an attribute reference: The primary expression in
  the reference is evaluated.  It should yield an object with
  assignable attributes; if this is not the case, "TypeError" is
  raised.  That object is then asked to assign the assigned object to
  the given attribute; if it cannot perform the assignment, it raises
  an exception (usually but not necessarily "AttributeError").

Note: If the object is a class instance and the attribute reference
  occurs on both sides of the assignment operator, the right-hand side
  expression, "a.x" can access either an instance attribute or (if no
  instance attribute exists) a class attribute.  The left-hand side
  target "a.x" is always set as an instance attribute, creating it if
  necessary.  Thus, the two occurrences of "a.x" do not necessarily
  refer to the same attribute: if the right-hand side expression
  refers to a class attribute, the left-hand side creates a new
  instance attribute as the target of the assignment:

class Cls:
         x = 3             # class variable
     inst = Cls()
     inst.x = inst.x + 1   # writes inst.x as 4 leaving Cls.x as 3

This description does not necessarily apply to descriptor
  attributes, such as properties created with "property()".

* If the target is a subscription: The primary expression in the
  reference is evaluated.  It should yield either a mutable sequence
  object (such as a list) or a mapping object (such as a dictionary).
  Next, the subscript expression is evaluated.

If the primary is a mutable sequence object (such as a list), the
  subscript must yield an integer.  If it is negative, the sequence’s
  length is added to it.  The resulting value must be a nonnegative
  integer less than the sequence’s length, and the sequence is asked
  to assign the assigned object to its item with that index.  If the
  index is out of range, "IndexError" is raised (assignment to a
  subscripted sequence cannot add new items to a list).

If the primary is a mapping object (such as a dictionary), the
  subscript must have a type compatible with the mapping’s key type,
  and the mapping is then asked to create a key/value pair which maps
  the subscript to the assigned object.  This can either replace an
  existing key/value pair with the same key value, or insert a new
  key/value pair (if no key with the same value existed).

For user-defined objects, the "__setitem__()" method is called with
  appropriate arguments.

* If the target is a slicing: The primary expression in the reference
  is evaluated.  It should yield a mutable sequence object (such as a
  list).  The assigned object should be a sequence object of the same
  type.  Next, the lower and upper bound expressions are evaluated,
  insofar they are present; defaults are zero and the sequence’s
  length.  The bounds should evaluate to integers. If either bound is
  negative, the sequence’s length is added to it.  The resulting
  bounds are clipped to lie between zero and the sequence’s length,
  inclusive.  Finally, the sequence object is asked to replace the
  slice with the items of the assigned sequence.  The length of the
  slice may be different from the length of the assigned sequence,
  thus changing the length of the target sequence, if the target
  sequence allows it.

**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.

Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion.  For instance, the following program prints
"[0, 2]":

x = [0, 1]
   i = 0
   i, x[i] = 1, 2         # i is updated, then x[i] is updated
   print(x)

See also:

**PEP 3132** - Extended Iterable Unpacking
     The specification for the "*target" feature.

Augmented assignment statements
===============================

Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:

augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
   augtarget                 ::= identifier | attributeref | subscription | slicing
   augop                     ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
                                 | ">>=" | "<<=" | "&=" | "^=" | "|="

(See section Primaries for the syntax definitions of the last three
symbols.)

An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.

An augmented assignment statement like "x += 1" can be rewritten as "x
= x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side.  For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".

With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.

For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.

Annotated assignment statements
===============================

*Annotation* assignment is the combination, in a single statement, of
a variable or attribute annotation and an optional assignment
statement:

annotated_assignment_stmt ::= augtarget ":" expression
                                 ["=" (starred_expression | yield_expression)]

The difference from normal Assignment statements is that only a single
target is allowed.

The assignment target is considered “simple” if it consists of a
single name that is not enclosed in parentheses. For simple assignment
targets, if in class or module scope, the annotations are evaluated
and stored in a special class or module attribute "__annotations__"
that is a dictionary mapping from variable names (mangled if private)
to evaluated annotations. This attribute is writable and is
automatically created at the start of class or module body execution,
if annotations are found statically.

If the assignment target is not simple (an attribute, subscript node,
or parenthesized name), the annotation is evaluated if in class or
module scope, but not stored.

If a name is annotated in a function scope, then this name is local
for that scope. Annotations are never evaluated and stored in function
scopes.

If the right hand side is present, an annotated assignment performs
the actual assignment before evaluating annotations (where
applicable). If the right hand side is not present for an expression
target, then the interpreter evaluates the target except for the last
"__setitem__()" or "__setattr__()" call.

See also:

**PEP 526** - Syntax for Variable Annotations
     The proposal that added syntax for annotating the types of
     variables (including class variables and instance variables),
     instead of expressing them through comments.

**PEP 484** - Type hints
     The proposal that added the "typing" module to provide a standard
     syntax for type annotations that can be used in static analysis
     tools and IDEs.

Changed in version 3.8: Now annotated assignments allow the same
expressions in the right hand side as regular assignments. Previously,
some expressions (like un-parenthesized tuple expressions) caused a
syntax error.
zassignment-expressionsu���Assignment expressions
**********************

assignment_expression ::= [identifier ":="] expression

An assignment expression (sometimes also called a “named expression”
or “walrus”) assigns an "expression" to an "identifier", while also
returning the value of the "expression".

One common use case is when handling matched regular expressions:

if matching := pattern.search(data):
       do_something(matching)

Or, when processing a file stream in chunks:

while chunk := file.read(9000):
       process(chunk)

Assignment expressions must be surrounded by parentheses when used as
expression statements and when used as sub-expressions in slicing,
conditional, lambda, keyword-argument, and comprehension-if
expressions and in "assert", "with", and "assignment" statements. In
all other places where they can be used, parentheses are not required,
including in "if" and "while" statements.

Added in version 3.8: See **PEP 572** for more details about
assignment expressions.
�asynca8��Coroutines
**********

Added in version 3.5.

Coroutine function definition
=============================

async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
                     ["->" expression] ":" suite

Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). "await" expressions, "async for" and "async
with" can only be used in the body of a coroutine function.

Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.

It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.

An example of a coroutine function:

async def func(param1, param2):
       do_stuff()
       await some_coroutine()

Changed in version 3.7: "await" and "async" are now keywords;
previously they were only treated as such inside the body of a
coroutine function.

The "async for" statement
=========================

async_for_stmt ::= "async" for_stmt

An *asynchronous iterable* provides an "__aiter__" method that
directly returns an *asynchronous iterator*, which can call
asynchronous code in its "__anext__" method.

The "async for" statement allows convenient iteration over
asynchronous iterables.

The following code:

async for TARGET in ITER:
       SUITE
   else:
       SUITE2

Is semantically equivalent to:

iter = (ITER).__aiter__()
   running = True

while running:
       try:
           TARGET = await iter.__anext__()
       except StopAsyncIteration:
           running = False
       else:
           SUITE
   else:
       SUITE2

except that implicit special method lookup is used for "__aiter__()"
and "__anext__()".

It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.

The "async with" statement
==========================

async_with_stmt ::= "async" with_stmt

An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.

The following code:

async with EXPRESSION as TARGET:
       SUITE

is semantically equivalent to:

manager = (EXPRESSION)
   aenter = manager.__aenter__
   aexit = manager.__aexit__
   value = await aenter()
   hit_except = False

try:
       TARGET = value
       SUITE
   except:
       hit_except = True
       if not await aexit(*sys.exc_info()):
           raise
   finally:
       if not hit_except:
           await aexit(None, None, None)

except that implicit special method lookup is used for "__aenter__()"
and "__aexit__()".

It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.

See also:

**PEP 492** - Coroutines with async and await syntax
     The proposal that made coroutines a proper standalone concept in
     Python, and added supporting syntax.
zatom-identifiersa���Identifiers (Names)
*******************

An identifier occurring as an atom is a name.  See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.

Private name mangling
=====================

When an identifier that textually occurs in a class definition begins
with two or more underscore characters and does not end in two or more
underscores, it is considered a *private name* of that class.

See also:

**PEP 562** - Module __getattr__ and __dir__
     Describes the "__getattr__" and "__dir__" functions on modules.

Implementing Descriptors
========================

The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents).  In the
examples below, “the attribute” refers to the attribute whose name is
the key of the property in the owner class’ "__dict__".  The "object"
class itself does not implement any of these protocols.

object.__get__(self, instance, owner=None)

Called to get the attribute of the owner class (class attribute
   access) or of an instance of that class (instance attribute
   access). The optional *owner* argument is the owner class, while
   *instance* is the instance that the attribute was accessed through,
   or "None" when the attribute is accessed through the *owner*.

This method should return the computed attribute value or raise an
   "AttributeError" exception.

**PEP 252** specifies that "__get__()" is callable with one or two
   arguments.  Python’s own built-in descriptors support this
   specification; however, it is likely that some third-party tools
   have descriptors that require both arguments.  Python’s own
   "__getattribute__()" implementation always passes in both arguments
   whether they are required or not.

object.__set__(self, instance, value)

Called to set the attribute on an instance *instance* of the owner
   class to a new value, *value*.

Note, adding "__set__()" or "__delete__()" changes the kind of
   descriptor to a “data descriptor”.  See Invoking Descriptors for
   more details.

object.__delete__(self, instance)

Called to delete the attribute on an instance *instance* of the
   owner class.

Instances of descriptors may also have the "__objclass__" attribute
present:

object.__objclass__

The attribute "__objclass__" is interpreted by the "inspect" module
   as specifying the class where this object was defined (setting this
   appropriately can assist in runtime introspection of dynamic class
   attributes). For callables, it may indicate that an instance of the
   given type (or a subclass) is expected or required as the first
   positional argument (for example, CPython sets this attribute for
   unbound methods that are implemented in C).

Invoking Descriptors
====================

In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol:  "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.

However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead.  Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.

The starting point for descriptor invocation is a binding, "a.x". How
the arguments are assembled depends on "a":

Direct Call
   The simplest and least common call is when user code directly
   invokes a descriptor method:    "x.__get__(a)".

Instance Binding
   If binding to an object instance, "a.x" is transformed into the
   call: "type(a).__dict__['x'].__get__(a, type(a))".

Class Binding
   If binding to a class, "A.x" is transformed into the call:
   "A.__dict__['x'].__get__(None, A)".

Super Binding
   A dotted lookup such as "super(A, a).x" searches
   "a.__class__.__mro__" for a base class "B" following "A" and then
   returns "B.__dict__['x'].__get__(a, A)".  If not a descriptor, "x"
   is returned unchanged.

For instance bindings, the precedence of descriptor invocation depends
on which descriptor methods are defined.  A descriptor can define any
combination of "__get__()", "__set__()" and "__delete__()".  If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary.  If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor.  Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method.  Data descriptors with "__get__()" and "__set__()"
(and/or "__delete__()") defined always override a redefinition in an
instance dictionary.  In contrast, non-data descriptors can be
overridden by instances.

Python methods (including those decorated with "@staticmethod" and
"@classmethod") are implemented as non-data descriptors.  Accordingly,
instances can redefine and override methods.  This allows individual
instances to acquire behaviors that differ from other instances of the
same class.

The "property()" function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.

__slots__
=========

*__slots__* allow us to explicitly declare data members (like
properties) and deny the creation of "__dict__" and *__weakref__*
(unless explicitly declared in *__slots__* or available in a parent.)

The space saved over using "__dict__" can be significant. Attribute
lookup speed can be significantly improved as well.

object.__slots__

This class variable can be assigned a string, iterable, or sequence
   of strings with variable names used by instances.  *__slots__*
   reserves space for the declared variables and prevents the
   automatic creation of "__dict__" and *__weakref__* for each
   instance.

Notes on using *__slots__*:

* When inheriting from a class without *__slots__*, the "__dict__" and
  *__weakref__* attribute of the instances will always be accessible.

* Without a "__dict__" variable, instances cannot be assigned new
  variables not listed in the *__slots__* definition.  Attempts to
  assign to an unlisted variable name raises "AttributeError". If
  dynamic assignment of new variables is desired, then add
  "'__dict__'" to the sequence of strings in the *__slots__*
  declaration.

* Without a *__weakref__* variable for each instance, classes defining
  *__slots__* do not support "weak references" to its instances. If
  weak reference support is needed, then add "'__weakref__'" to the
  sequence of strings in the *__slots__* declaration.

* *__slots__* are implemented at the class level by creating
  descriptors for each variable name.  As a result, class attributes
  cannot be used to set default values for instance variables defined
  by *__slots__*; otherwise, the class attribute would overwrite the
  descriptor assignment.

* The action of a *__slots__* declaration is not limited to the class
  where it is defined.  *__slots__* declared in parents are available
  in child classes. However, instances of a child subclass will get a
  "__dict__" and *__weakref__* unless the subclass also defines
  *__slots__* (which should only contain names of any *additional*
  slots).

* If a class defines a slot also defined in a base class, the instance
  variable defined by the base class slot is inaccessible (except by
  retrieving its descriptor directly from the base class). This
  renders the meaning of the program undefined.  In the future, a
  check may be added to prevent this.

* "TypeError" will be raised if nonempty *__slots__* are defined for a
  class derived from a ""variable-length" built-in type" such as
  "int", "bytes", and "tuple".

* Any non-string *iterable* may be assigned to *__slots__*.

* If a "dictionary" is used to assign *__slots__*, the dictionary keys
  will be used as the slot names. The values of the dictionary can be
  used to provide per-attribute docstrings that will be recognised by
  "inspect.getdoc()" and displayed in the output of "help()".

* "__class__" assignment works only if both classes have the same
  *__slots__*.

* Multiple inheritance with multiple slotted parent classes can be
  used, but only one parent is allowed to have attributes created by
  slots (the other bases must have empty slot layouts) - violations
  raise "TypeError".

* If an *iterator* is used for *__slots__* then a *descriptor* is
  created for each of the iterator’s values. However, the *__slots__*
  attribute will be an empty iterator.
zattribute-referencesaU��Attribute references
********************

An attribute reference is a primary followed by a period and a name:

attributeref ::= primary "." identifier

The primary must evaluate to an object of a type that supports
attribute references, which most objects do.  This object is then
asked to produce the attribute whose name is the identifier. The type
and value produced is determined by the object.  Multiple evaluations
of the same attribute reference may yield different objects.

This production can be customized by overriding the
"__getattribute__()" method or the "__getattr__()" method.  The
"__getattribute__()" method is called first and either returns a value
or raises "AttributeError" if the attribute is not available.

If an "AttributeError" is raised and the object has a "__getattr__()"
method, that method is called as a fallback.
�	augassigna���Augmented assignment statements
*******************************

Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:

augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
   augtarget                 ::= identifier | attributeref | subscription | slicing
   augop                     ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
                                 | ">>=" | "<<=" | "&=" | "^=" | "|="

(See section Primaries for the syntax definitions of the last three
symbols.)

An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.

An augmented assignment statement like "x += 1" can be rewritten as "x
= x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side.  For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".

With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.

For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
�awaitz�Await expression
****************

Suspend the execution of *coroutine* on an *awaitable* object. Can
only be used inside a *coroutine function*.

await_expr ::= "await" primary

Added in version 3.5.
�binaryu��Binary arithmetic operations
****************************

The binary arithmetic operations have the conventional priority
levels.  Note that some of these operations also apply to certain non-
numeric types.  Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:

The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.

This operation can be customized using the special "__mul__()" and
"__rmul__()" methods.

The "@" (at) operator is intended to be used for matrix
multiplication.  No builtin Python types implement this operator.

This operation can be customized using the special "__matmul__()" and
"__rmatmul__()" methods.

Added in version 3.5.

The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments.  The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result.  Division by zero raises the "ZeroDivisionError" exception.

The division operation can be customized using the special
"__truediv__()" and "__rtruediv__()" methods. The floor division
operation can be customized using the special "__floordiv__()" and
"__rfloordiv__()" methods.

The "%" (modulo) operator yields the remainder from the division of
the first argument by the second.  The numeric arguments are first
converted to a common type.  A zero right argument raises the
"ZeroDivisionError" exception.  The arguments may be floating-point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".)  The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].

The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)".  Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].

In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation).  The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.

The *modulo* operation can be customized using the special "__mod__()"
and "__rmod__()" methods.

The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers.  Instead, convert to a
floating-point number using the "abs()" function if appropriate.

The "+" (addition) operator yields the sum of its arguments.  The
arguments must either both be numbers or both be sequences of the same
type.  In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.

This operation can be customized using the special "__add__()" and
"__radd__()" methods.

The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.

This operation can be customized using the special "__sub__()" and
"__rsub__()" methods.
�bitwisea<��Binary bitwise operations
*************************

Each of the three bitwise operations has a different priority level:

and_expr ::= shift_expr | and_expr "&" shift_expr
   xor_expr ::= and_expr | xor_expr "^" and_expr
   or_expr  ::= xor_expr | or_expr "|" xor_expr

The "&" operator yields the bitwise AND of its arguments, which must
be integers or one of them must be a custom object overriding
"__and__()" or "__rand__()" special methods.

The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers or one of them must be a custom
object overriding "__xor__()" or "__rxor__()" special methods.

The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers or one of them must be a custom object
overriding "__or__()" or "__ror__()" special methods.
zbltin-code-objectsu���Code Objects
************

Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment.  Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.

Accessing "__code__" raises an auditing event "object.__getattr__"
with arguments "obj" and ""__code__"".

A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()"  built-in functions.

See The standard type hierarchy for more information.
zbltin-ellipsis-objectu���The Ellipsis Object
*******************

This object is commonly used to indicate that something is omitted. It
supports no special operations.  There is exactly one ellipsis object,
named "Ellipsis" (a built-in name).  "type(Ellipsis)()" produces the
"Ellipsis" singleton.

It is written as "Ellipsis" or "...".

In typical use, "..." as the "Ellipsis" object appears in a few
different places, for instance:

* In type annotations, such as callable arguments or tuple elements.

* As the body of a function instead of a pass statement.

* In third-party libraries, such as Numpy’s slicing and striding.

Python also uses three dots in ways that are not "Ellipsis" objects,
for instance:

* Doctest’s "ELLIPSIS", as a pattern for missing content.

* The default Python prompt of the *interactive* shell when partial
  input is incomplete.

Lastly, the Python documentation often uses three dots in conventional
English usage to mean omitted content, even in code examples that also
use them as the "Ellipsis".
zbltin-null-objectu��The Null Object
***************

This object is returned by functions that don’t explicitly return a
value.  It supports no special operations.  There is exactly one null
object, named "None" (a built-in name).  "type(None)()" produces the
same singleton.

It is written as "None".
zbltin-type-objectsu5��Type Objects
************

Type objects represent the various object types.  An object’s type is
accessed by the built-in function "type()".  There are no special
operations on types.  The standard module "types" defines names for
all standard built-in types.

Types are written like this: "<class 'int'>".
�booleansa���Boolean operations
******************

or_test  ::= and_test | or_test "or" and_test
   and_test ::= not_test | and_test "and" not_test
   not_test ::= comparison | "not" not_test

In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets).  All other values are interpreted
as true.  User-defined objects can customize their truth value by
providing a "__bool__()" method.

The operator "not" yields "True" if its argument is false, "False"
otherwise.

The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.

The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.

Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument.  This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value.  Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
�breaka$��The "break" statement
*********************

break_stmt ::= "break"

"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.

It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.

If a "for" loop is terminated by "break", the loop control target
keeps its current value.

When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
zcallable-typesu=��Emulating callable objects
**************************

object.__call__(self[, args...])

Called when the instance is “called” as a function; if this method
   is defined, "x(arg1, arg2, ...)" roughly translates to
   "type(x).__call__(x, arg1, ...)". The "object" class itself does
   not provide this method.
�callsu5��Calls
*****

A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:

call                 ::= primary "(" [argument_list [","] | comprehension] ")"
   argument_list        ::= positional_arguments ["," starred_and_keywords]
                            ["," keywords_arguments]
                            | starred_and_keywords ["," keywords_arguments]
                            | keywords_arguments
   positional_arguments ::= positional_item ("," positional_item)*
   positional_item      ::= assignment_expression | "*" expression
   starred_and_keywords ::= ("*" expression | keyword_item)
                            ("," "*" expression | "," keyword_item)*
   keywords_arguments   ::= (keyword_item | "**" expression)
                            ("," keyword_item | "," "**" expression)*
   keyword_item         ::= identifier "=" expression

An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable).  All argument expressions are
evaluated before the call is attempted.  Please refer to section
Function definitions for the syntax of formal *parameter* lists.

If keyword arguments are present, they are first converted to
positional arguments, as follows.  First, a list of unfilled slots is
created for the formal parameters.  If there are N positional
arguments, they are placed in the first N slots.  Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on).  If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
argument is placed in the slot, filling it (even if the expression is
"None", it fills the slot).  When all arguments have been processed,
the slots that are still unfilled are filled with the corresponding
default value from the function definition.  (Default values are
calculated, once, when the function is defined; thus, a mutable object
such as a list or dictionary used as default value will be shared by
all calls that don’t specify an argument value for the corresponding
slot; this should usually be avoided.)  If there are any unfilled
slots for which no default value is specified, a "TypeError" exception
is raised.  Otherwise, the list of filled slots is used as the
argument list for the call.

**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword.  In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.

If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.

If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*.  Elements from these iterables are
treated as if they were additional positional arguments.  For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.

A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:

>>> def f(a, b):
   ...     print(a, b)
   ...
   >>> f(b=1, *(2,))
   2 1
   >>> f(a=1, *(2,))
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: f() got multiple values for keyword argument 'a'
   >>> f(1, *(2,))
   1 2

It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
often arise.

If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments. If a parameter matching a key
has already been given a value (by an explicit keyword argument, or
from another unpacking), a "TypeError" exception is raised.

When "**expression" is used, each key in this mapping must be a
string. Each value from the mapping is assigned to the first formal
parameter eligible for keyword assignment whose name is equal to the
key. A key need not be a Python identifier (e.g. ""max-temp °F"" is
acceptable, although it will not match any formal parameter that could
be declared). If there is no match to a formal parameter the key-value
pair is collected by the "**" parameter, if there is one, or if there
is not, a "TypeError" exception is raised.

Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.

Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.

A call always returns some value, possibly "None", unless it raises an
exception.  How this value is computed depends on the type of the
callable object.

If it is—

a user-defined function:
   The code block for the function is executed, passing it the
   argument list.  The first thing the code block will do is bind the
   formal parameters to the arguments; this is described in section
   Function definitions.  When the code block executes a "return"
   statement, this specifies the return value of the function call.
   If execution reaches the end of the code block without executing a
   "return" statement, the return value is "None".

a built-in function or method:
   The result is up to the interpreter; see Built-in Functions for the
   descriptions of built-in functions and methods.

a class object:
   A new instance of that class is returned.

a class instance method:
   The corresponding user-defined function is called, with an argument
   list that is one longer than the argument list of the call: the
   instance becomes the first argument.

a class instance:
   The class must define a "__call__()" method; the effect is then the
   same as if that method was called.
�classu)
��Class definitions
*****************

A class definition defines a class object (see section The standard
type hierarchy):

classdef    ::= [decorators] "class" classname [type_params] [inheritance] ":" suite
   inheritance ::= "(" [argument_list] ")"
   classname   ::= identifier

A class definition is an executable statement.  The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing.  Classes without an inheritance list
inherit, by default, from the base class "object"; hence,

class Foo:
       pass

is equivalent to

class Foo(object):
       pass

The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.)  When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [5] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.

The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__".  Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.

Class creation can be customized heavily using metaclasses.

Classes can also be decorated: just like when decorating functions,

@f1(arg)
   @f2
   class Foo: pass

is roughly equivalent to

class Foo: pass
   Foo = f1(arg)(f2(Foo))

The evaluation rules for the decorator expressions are the same as for
function decorators.  The result is then bound to the class name.

Changed in version 3.9: Classes may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.

A list of type parameters may be given in square brackets immediately
after the class’s name. This indicates to static type checkers that
the class is generic. At runtime, the type parameters can be retrieved
from the class’s "__type_params__" attribute. See Generic classes for
more.

Changed in version 3.12: Type parameter lists are new in Python 3.12.

**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances.  Instance attributes
can be set in a method with "self.name = value".  Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way.  Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results.  Descriptors can be used to create instance
variables with different implementation details.

See also:

**PEP 3115** - Metaclasses in Python 3000
     The proposal that changed the declaration of metaclasses to the
     current syntax, and the semantics for how classes with
     metaclasses are constructed.

**PEP 3129** - Class Decorators
     The proposal that added class decorators.  Function and method
     decorators were introduced in **PEP 318**.
�comparisonsu(��Comparisons
***********

Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation.  Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:

comparison    ::= or_expr (comp_operator or_expr)*
   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
                     | "is" ["not"] | ["not"] "in"

Comparisons yield boolean values: "True" or "False". Custom *rich
comparison methods* may return non-boolean values. In this case Python
will call "bool()" on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).

Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.

Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).

Value comparisons
=================

The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects.  The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in
addition to type and identity).  The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value.  Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is.  One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.

Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object".  Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.

The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects.  Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality.  A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").

A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError".  A motivation for this default behavior
is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality.  Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.

The following list describes the comparison behavior of the most
important built-in types.

* Numbers of built-in numeric types (Numeric Types — int, float,
  complex) and of the standard library types "fractions.Fraction" and
  "decimal.Decimal" can be compared within and across their types,
  with the restriction that complex numbers do not support order
  comparison.  Within the limits of the types involved, they compare
  mathematically (algorithmically) correct without loss of precision.

The not-a-number values "float('NaN')" and "decimal.Decimal('NaN')"
  are special.  Any ordered comparison of a number to a not-a-number
  value is false. A counter-intuitive implication is that not-a-number
  values are not equal to themselves.  For example, if "x =
  float('NaN')", "3 < x", "x < 3" and "x == x" are all false, while "x
  != x" is true.  This behavior is compliant with IEEE 754.

* "None" and "NotImplemented" are singletons.  **PEP 8** advises that
  comparisons for singletons should always be done with "is" or "is
  not", never the equality operators.

* Binary sequences (instances of "bytes" or "bytearray") can be
  compared within and across their types.  They compare
  lexicographically using the numeric values of their elements.

* Strings (instances of "str") compare lexicographically using the
  numerical Unicode code points (the result of the built-in function
  "ord()") of their characters. [3]

Strings and binary sequences cannot be directly compared.

* Sequences (instances of "tuple", "list", or "range") can be compared
  only within each of their types, with the restriction that ranges do
  not support order comparison.  Equality comparison across these
  types results in inequality, and ordering comparison across these
  types raises "TypeError".

Sequences compare lexicographically using comparison of
  corresponding elements.  The built-in containers typically assume
  identical objects are equal to themselves.  That lets them bypass
  equality tests for identical objects to improve performance and to
  maintain their internal invariants.

Lexicographical comparison between built-in collections works as
  follows:

* For two collections to compare equal, they must be of the same
    type, have the same length, and each pair of corresponding
    elements must compare equal (for example, "[1,2] == (1,2)" is
    false because the type is not the same).

* Collections that support order comparison are ordered the same as
    their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
    has the same value as "x <= y").  If a corresponding element does
    not exist, the shorter collection is ordered first (for example,
    "[1,2] < [1,2,3]" is true).

* Mappings (instances of "dict") compare equal if and only if they
  have equal "(key, value)" pairs. Equality comparison of the keys and
  values enforces reflexivity.

Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".

* Sets (instances of "set" or "frozenset") can be compared within and
  across their types.

They define order comparison operators to mean subset and superset
  tests.  Those relations do not define total orderings (for example,
  the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
  another, nor supersets of one another).  Accordingly, sets are not
  appropriate arguments for functions which depend on total ordering
  (for example, "min()", "max()", and "sorted()" produce undefined
  results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

* Most other built-in types have no comparison methods implemented, so
  they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:

* Equality comparison should be reflexive. In other words, identical
  objects should compare equal:

"x is y" implies "x == y"

* Comparison should be symmetric. In other words, the following
  expressions should have the same result:

"x == y" and "y == x"

"x != y" and "y != x"

"x < y" and "y > x"

"x <= y" and "y >= x"

* Comparison should be transitive. The following (non-exhaustive)
  examples illustrate that:

"x > y and y > z" implies "x > z"

"x < y and y <= z" implies "x < z"

* Inverse comparison should result in the boolean negation. In other
  words, the following expressions should have the same result:

"x == y" and "not x != y"

"x < y" and "not x >= y" (for total ordering)

"x > y" and "not x <= y" (for total ordering)

The last two expressions apply to totally ordered collections (e.g.
  to sequences, but not to sets or mappings). See also the
  "total_ordering()" decorator.

* The "hash()" result should be consistent with equality. Objects that
  are equal should either have the same hash value, or be marked as
  unhashable.

Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.

Membership test operations
==========================

The operators "in" and "not in" test for membership.  "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s".  All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".

For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*.  An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".

For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.

For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception.  (If any other
exception is raised, it is as if "in" raised that exception).

The operator "not in" is defined to have the inverse truth value of
"in".

Identity comparisons
====================

The operators "is" and "is not" test for an object’s identity: "x is
y" is true if and only if *x* and *y* are the same object.  An
Object’s identity is determined using the "id()" function.  "x is not
y" yields the inverse truth value. [4]
�compoundu,���Compound statements
*******************

Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way.  In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.

The "if", "while" and "for" statements implement traditional control
flow constructs.  "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code.  Function and class definitions are also syntactically compound
statements.

A compound statement consists of one or more ‘clauses.’  A clause
consists of a header and a ‘suite.’  The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon.  A suite is a group of statements controlled by a
clause.  A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines.  Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:

if test1: if test2: print(x)

Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:

if x < y < z: print(x); print(y); print(z)

Summarizing:

Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT".  Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).

The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.

The "if" statement
==================

The "if" statement is used for conditional execution:

if_stmt ::= "if" assignment_expression ":" suite
               ("elif" assignment_expression ":" suite)*
               ["else" ":" suite]

It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.

The "while" statement
=====================

The "while" statement is used for repeated execution as long as an
expression is true:

while_stmt ::= "while" assignment_expression ":" suite
                  ["else" ":" suite]

This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite.  A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.

The "for" statement
===================

The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:

for_stmt ::= "for" target_list "in" `!starred_list` ":" suite
                ["else" ":" suite]

The "starred_list" expression is evaluated once; it should yield an
*iterable* object.  An *iterator* is created for that iterable. The
first item provided by the iterator is then assigned to the target
list using the standard rules for assignments (see Assignment
statements), and the suite is executed.  This repeats for each item
provided by the iterator.  When the iterator is exhausted, the suite
in the "else" clause, if present, is executed, and the loop
terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite.  A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.

The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:

for i in range(10):
       print(i)
       i = 5             # this will not affect the for-loop
                         # because i will be overwritten with the next
                         # index in the range

Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop.  Hint: the built-in type "range()" represents
immutable arithmetic sequences of integers. For instance, iterating
"range(3)" successively yields 0, 1, and then 2.

Changed in version 3.11: Starred elements are now allowed in the
expression list.

The "try" statement
===================

The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:

try_stmt  ::= try1_stmt | try2_stmt | try3_stmt
   try1_stmt ::= "try" ":" suite
                 ("except" [expression ["as" identifier]] ":" suite)+
                 ["else" ":" suite]
                 ["finally" ":" suite]
   try2_stmt ::= "try" ":" suite
                 ("except" "*" expression ["as" identifier] ":" suite)+
                 ["else" ":" suite]
                 ["finally" ":" suite]
   try3_stmt ::= "try" ":" suite
                 "finally" ":" suite

Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.

"except" clause
---------------

The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the "except"
clauses in turn until one is found that matches the exception. An
expression-less "except" clause, if present, must be last; it matches
any exception.

For an "except" clause with an expression, the expression must
evaluate to an exception type or a tuple of exception types. The
raised exception matches an "except" clause whose expression evaluates
to the class or a *non-virtual base class* of the exception object, or
to a tuple that contains such a class.

If no "except" clause matches the exception, the search for an
exception handler continues in the surrounding code and on the
invocation stack.  [1]

If the evaluation of an expression in the header of an "except" clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).

When a matching "except" clause is found, the exception is assigned to
the target specified after the "as" keyword in that "except" clause,
if present, and the "except" clause’s suite is executed. All "except"
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire "try"
statement. (This means that if two nested handlers exist for the same
exception, and the exception occurs in the "try" clause of the inner
handler, the outer handler will not handle the exception.)

When an exception has been assigned using "as target", it is cleared
at the end of the "except" clause.  This is as if

except E as N:
       foo

was translated to

except E as N:
       try:
           foo
       finally:
           del N

This means the exception must be assigned to a different name to be
able to refer to it after the "except" clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.

Before an "except" clause’s suite is executed, the exception is stored
in the "sys" module, where it can be accessed from within the body of
the "except" clause by calling "sys.exception()". When leaving an
exception handler, the exception stored in the "sys" module is reset
to its previous value:

>>> print(sys.exception())
   None
   >>> try:
   ...     raise TypeError
   ... except:
   ...     print(repr(sys.exception()))
   ...     try:
   ...          raise ValueError
   ...     except:
   ...         print(repr(sys.exception()))
   ...     print(repr(sys.exception()))
   ...
   TypeError()
   ValueError()
   TypeError()
   >>> print(sys.exception())
   None

"except*" clause
----------------

The "except*" clause(s) specify one or more handlers for groups of
exceptions ("BaseExceptionGroup" instances). A "try" statement can
have either "except" or "except*" clauses, but not both. The exception
type for matching is mandatory in the case of "except*", so "except*:"
is a syntax error. The type is interpreted as in the case of "except",
but matching is performed on the exceptions contained in the group
that is being handled. An "TypeError" is raised if a matching type is
a subclass of "BaseExceptionGroup", because that would have ambiguous
semantics.

When an exception group is raised in the try block, each "except*"
clause splits (see "split()") it into the subgroups of matching and
non-matching exceptions. If the matching subgroup is not empty, it
becomes the handled exception (the value returned from
"sys.exception()") and assigned to the target of the "except*" clause
(if there is one). Then, the body of the "except*" clause executes. If
the non-matching subgroup is not empty, it is processed by the next
"except*" in the same manner. This continues until all exceptions in
the group have been matched, or the last "except*" clause has run.

After all "except*" clauses execute, the group of unhandled exceptions
is merged with any exceptions that were raised or re-raised from
within "except*" clauses. This merged exception group propagates on.:

>>> try:
   ...     raise ExceptionGroup("eg",
   ...         [ValueError(1), TypeError(2), OSError(3), OSError(4)])
   ... except* TypeError as e:
   ...     print(f'caught {type(e)} with nested {e.exceptions}')
   ... except* OSError as e:
   ...     print(f'caught {type(e)} with nested {e.exceptions}')
   ...
   caught <class 'ExceptionGroup'> with nested (TypeError(2),)
   caught <class 'ExceptionGroup'> with nested (OSError(3), OSError(4))
     + Exception Group Traceback (most recent call last):
     |   File "<doctest default[0]>", line 2, in <module>
     |     raise ExceptionGroup("eg",
     |         [ValueError(1), TypeError(2), OSError(3), OSError(4)])
     | ExceptionGroup: eg (1 sub-exception)
     +-+---------------- 1 ----------------
       | ValueError: 1
       +------------------------------------

If the exception raised from the "try" block is not an exception group
and its type matches one of the "except*" clauses, it is caught and
wrapped by an exception group with an empty message string. This
ensures that the type of the target "e" is consistently
"BaseExceptionGroup":

>>> try:
   ...     raise BlockingIOError
   ... except* BlockingIOError as e:
   ...     print(repr(e))
   ...
   ExceptionGroup('', (BlockingIOError(),))

"break", "continue" and "return" cannot appear in an "except*" clause.

"else" clause
-------------

The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed.  Exceptions in the "else" clause are
not handled by the preceding "except" clauses.

"finally" clause
----------------

If "finally" is present, it specifies a ‘cleanup’ handler.  The "try"
clause is executed, including any "except" and "else" clauses. If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed.  If
there is a saved exception it is re-raised at the end of the "finally"
clause. If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:

>>> def f():
   ...     try:
   ...         1/0
   ...     finally:
   ...         return 42
   ...
   >>> f()
   42

The exception information is not available to the program during
execution of the "finally" clause.

When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’

The return value of a function is determined by the last "return"
statement executed.  Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:

>>> def foo():
   ...     try:
   ...         return 'try'
   ...     finally:
   ...         return 'finally'
   ...
   >>> foo()
   'finally'

Changed in version 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.

The "with" statement
====================

The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.

with_stmt          ::= "with" ( "(" with_stmt_contents ","? ")" | with_stmt_contents ) ":" suite
   with_stmt_contents ::= with_item ("," with_item)*
   with_item          ::= expression ["as" target]

The execution of the "with" statement with one “item” proceeds as
follows:

1. The context expression (the expression given in the "with_item") is
   evaluated to obtain a context manager.

2. The context manager’s "__enter__()" is loaded for later use.

3. The context manager’s "__exit__()" is loaded for later use.

4. The context manager’s "__enter__()" method is invoked.

5. If a target was included in the "with" statement, the return value
   from "__enter__()" is assigned to it.

Note:

The "with" statement guarantees that if the "__enter__()" method
     returns without an error, then "__exit__()" will always be
     called. Thus, if an error occurs during the assignment to the
     target list, it will be treated the same as an error occurring
     within the suite would be. See step 7 below.

6. The suite is executed.

7. The context manager’s "__exit__()" method is invoked.  If an
   exception caused the suite to be exited, its type, value, and
   traceback are passed as arguments to "__exit__()". Otherwise, three
   "None" arguments are supplied.

If the suite was exited due to an exception, and the return value
   from the "__exit__()" method was false, the exception is reraised.
   If the return value was true, the exception is suppressed, and
   execution continues with the statement following the "with"
   statement.

If the suite was exited for any reason other than an exception, the
   return value from "__exit__()" is ignored, and execution proceeds
   at the normal location for the kind of exit that was taken.

The following code:

with EXPRESSION as TARGET:
       SUITE

is semantically equivalent to:

manager = (EXPRESSION)
   enter = manager.__enter__
   exit = manager.__exit__
   value = enter()
   hit_except = False

try:
       TARGET = value
       SUITE
   except:
       hit_except = True
       if not exit(*sys.exc_info()):
           raise
   finally:
       if not hit_except:
           exit(None, None, None)

except that implicit special method lookup is used for "__enter__()"
and "__exit__()".

With more than one item, the context managers are processed as if
multiple "with" statements were nested:

with A() as a, B() as b:
       SUITE

is semantically equivalent to:

with A() as a:
       with B() as b:
           SUITE

You can also write multi-item context managers in multiple lines if
the items are surrounded by parentheses. For example:

with (
       A() as a,
       B() as b,
   ):
       SUITE

Changed in version 3.1: Support for multiple context expressions.

Changed in version 3.10: Support for using grouping parentheses to
break the statement in multiple lines.

See also:

**PEP 343** - The “with” statement
     The specification, background, and examples for the Python "with"
     statement.

The "match" statement
=====================

Added in version 3.10.

The match statement is used for pattern matching.  Syntax:

match_stmt   ::= 'match' subject_expr ":" NEWLINE INDENT case_block+ DEDENT
   subject_expr ::= `!star_named_expression` "," `!star_named_expressions`?
                    | `!named_expression`
   case_block   ::= 'case' patterns [guard] ":" `!block`

Note:

This section uses single quotes to denote soft keywords.

Pattern matching takes a pattern as input (following "case") and a
subject value (following "match").  The pattern (which may contain
subpatterns) is matched against the subject value.  The outcomes are:

* A match success or failure (also termed a pattern success or
  failure).

* Possible binding of matched values to a name.  The prerequisites for
  this are further discussed below.

The "match" and "case" keywords are soft keywords.

See also:

* **PEP 634** – Structural Pattern Matching: Specification

* **PEP 636** – Structural Pattern Matching: Tutorial

Overview
--------

Here’s an overview of the logical flow of a match statement:

1. The subject expression "subject_expr" is evaluated and a resulting
   subject value obtained. If the subject expression contains a comma,
   a tuple is constructed using the standard rules.

2. Each pattern in a "case_block" is attempted to match with the
   subject value. The specific rules for success or failure are
   described below. The match attempt can also bind some or all of the
   standalone names within the pattern. The precise pattern binding
   rules vary per pattern type and are specified below.  **Name
   bindings made during a successful pattern match outlive the
   executed block and can be used after the match statement**.

Note:

During failed pattern matches, some subpatterns may succeed.  Do
     not rely on bindings being made for a failed match.  Conversely,
     do not rely on variables remaining unchanged after a failed
     match.  The exact behavior is dependent on implementation and may
     vary.  This is an intentional decision made to allow different
     implementations to add optimizations.

3. If the pattern succeeds, the corresponding guard (if present) is
   evaluated. In this case all name bindings are guaranteed to have
   happened.

* If the guard evaluates as true or is missing, the "block" inside
     "case_block" is executed.

* Otherwise, the next "case_block" is attempted as described above.

* If there are no further case blocks, the match statement is
     completed.

Note:

Users should generally never rely on a pattern being evaluated.
  Depending on implementation, the interpreter may cache values or use
  other optimizations which skip repeated evaluations.

A sample match statement:

>>> flag = False
   >>> match (100, 200):
   ...    case (100, 300):  # Mismatch: 200 != 300
   ...        print('Case 1')
   ...    case (100, 200) if flag:  # Successful match, but guard fails
   ...        print('Case 2')
   ...    case (100, y):  # Matches and binds y to 200
   ...        print(f'Case 3, y: {y}')
   ...    case _:  # Pattern not attempted
   ...        print('Case 4, I match anything!')
   ...
   Case 3, y: 200

In this case, "if flag" is a guard.  Read more about that in the next
section.

Guards
------

guard ::= "if" `!named_expression`

A "guard" (which is part of the "case") must succeed for code inside
the "case" block to execute.  It takes the form: "if" followed by an
expression.

The logical flow of a "case" block with a "guard" follows:

1. Check that the pattern in the "case" block succeeded.  If the
   pattern failed, the "guard" is not evaluated and the next "case"
   block is checked.

2. If the pattern succeeded, evaluate the "guard".

* If the "guard" condition evaluates as true, the case block is
     selected.

* If the "guard" condition evaluates as false, the case block is
     not selected.

* If the "guard" raises an exception during evaluation, the
     exception bubbles up.

Guards are allowed to have side effects as they are expressions.
Guard evaluation must proceed from the first to the last case block,
one at a time, skipping case blocks whose pattern(s) don’t all
succeed. (I.e., guard evaluation must happen in order.) Guard
evaluation must stop once a case block is selected.

Irrefutable Case Blocks
-----------------------

An irrefutable case block is a match-all case block.  A match
statement may have at most one irrefutable case block, and it must be
last.

A case block is considered irrefutable if it has no guard and its
pattern is irrefutable.  A pattern is considered irrefutable if we can
prove from its syntax alone that it will always succeed.  Only the
following patterns are irrefutable:

* AS Patterns whose left-hand side is irrefutable

* OR Patterns containing at least one irrefutable pattern

* Capture Patterns

* Wildcard Patterns

* parenthesized irrefutable patterns

Patterns
--------

Note:

This section uses grammar notations beyond standard EBNF:

* the notation "SEP.RULE+" is shorthand for "RULE (SEP RULE)*"

* the notation "!RULE" is shorthand for a negative lookahead
    assertion

The top-level syntax for "patterns" is:

The descriptions below will include a description “in simple terms” of
what a pattern does for illustration purposes (credits to Raymond
Hettinger for a document that inspired most of the descriptions). Note
that these descriptions are purely for illustration purposes and **may
not** reflect the underlying implementation.  Furthermore, they do not
cover all valid forms.

OR Patterns
~~~~~~~~~~~

An OR pattern is two or more patterns separated by vertical bars "|".
Syntax:

or_pattern ::= "|".closed_pattern+

Only the final subpattern may be irrefutable, and each subpattern must
bind the same set of names to avoid ambiguity.

An OR pattern matches each of its subpatterns in turn to the subject
value, until one succeeds.  The OR pattern is then considered
successful.  Otherwise, if none of the subpatterns succeed, the OR
pattern fails.

In simple terms, "P1 | P2 | ..." will try to match "P1", if it fails
it will try to match "P2", succeeding immediately if any succeeds,
failing otherwise.

AS Patterns
~~~~~~~~~~~

An AS pattern matches an OR pattern on the left of the "as" keyword
against a subject.  Syntax:

as_pattern ::= or_pattern "as" capture_pattern

If the OR pattern fails, the AS pattern fails.  Otherwise, the AS
pattern binds the subject to the name on the right of the as keyword
and succeeds. "capture_pattern" cannot be a "_".

In simple terms "P as NAME" will match with "P", and on success it
will set "NAME = <subject>".

Literal Patterns
~~~~~~~~~~~~~~~~

A literal pattern corresponds to most literals in Python.  Syntax:

The rule "strings" and the token "NUMBER" are defined in the standard
Python grammar.  Triple-quoted strings are supported.  Raw strings and
byte strings are supported.  f-strings are not supported.

The forms "signed_number '+' NUMBER" and "signed_number '-' NUMBER"
are for expressing complex numbers; they require a real number on the
left and an imaginary number on the right. E.g. "3 + 4j".

In simple terms, "LITERAL" will succeed only if "<subject> ==
LITERAL". For the singletons "None", "True" and "False", the "is"
operator is used.

Capture Patterns
~~~~~~~~~~~~~~~~

A capture pattern binds the subject value to a name. Syntax:

capture_pattern ::= !'_' NAME

A single underscore "_" is not a capture pattern (this is what "!'_'"
expresses). It is instead treated as a "wildcard_pattern".

In a given pattern, a given name can only be bound once.  E.g. "case
x, x: ..." is invalid while "case [x] | x: ..." is allowed.

Capture patterns always succeed.  The binding follows scoping rules
established by the assignment expression operator in **PEP 572**; the
name becomes a local variable in the closest containing function scope
unless there’s an applicable "global" or "nonlocal" statement.

In simple terms "NAME" will always succeed and it will set "NAME =
<subject>".

Wildcard Patterns
~~~~~~~~~~~~~~~~~

A wildcard pattern always succeeds (matches anything) and binds no
name.  Syntax:

wildcard_pattern ::= '_'

"_" is a soft keyword within any pattern, but only within patterns.
It is an identifier, as usual, even within "match" subject
expressions, "guard"s, and "case" blocks.

In simple terms, "_" will always succeed.

Value Patterns
~~~~~~~~~~~~~~

A value pattern represents a named value in Python. Syntax:

value_pattern ::= attr
   attr          ::= name_or_attr "." NAME
   name_or_attr  ::= attr | NAME

The dotted name in the pattern is looked up using standard Python name
resolution rules.  The pattern succeeds if the value found compares
equal to the subject value (using the "==" equality operator).

In simple terms "NAME1.NAME2" will succeed only if "<subject> ==
NAME1.NAME2"

Note:

If the same value occurs multiple times in the same match statement,
  the interpreter may cache the first value found and reuse it rather
  than repeat the same lookup.  This cache is strictly tied to a given
  execution of a given match statement.

Group Patterns
~~~~~~~~~~~~~~

A group pattern allows users to add parentheses around patterns to
emphasize the intended grouping.  Otherwise, it has no additional
syntax. Syntax:

group_pattern ::= "(" pattern ")"

In simple terms "(P)" has the same effect as "P".

Sequence Patterns
~~~~~~~~~~~~~~~~~

A sequence pattern contains several subpatterns to be matched against
sequence elements. The syntax is similar to the unpacking of a list or
tuple.

sequence_pattern       ::= "[" [maybe_sequence_pattern] "]"
                              | "(" [open_sequence_pattern] ")"
   open_sequence_pattern  ::= maybe_star_pattern "," [maybe_sequence_pattern]
   maybe_sequence_pattern ::= ",".maybe_star_pattern+ ","?
   maybe_star_pattern     ::= star_pattern | pattern
   star_pattern           ::= "*" (capture_pattern | wildcard_pattern)

There is no difference if parentheses  or square brackets are used for
sequence patterns (i.e. "(...)" vs "[...]" ).

Note:

A single pattern enclosed in parentheses without a trailing comma
  (e.g. "(3 | 4)") is a group pattern. While a single pattern enclosed
  in square brackets (e.g. "[3 | 4]") is still a sequence pattern.

At most one star subpattern may be in a sequence pattern.  The star
subpattern may occur in any position. If no star subpattern is
present, the sequence pattern is a fixed-length sequence pattern;
otherwise it is a variable-length sequence pattern.

The following is the logical flow for matching a sequence pattern
against a subject value:

1. If the subject value is not a sequence [2], the sequence pattern
   fails.

2. If the subject value is an instance of "str", "bytes" or
   "bytearray" the sequence pattern fails.

3. The subsequent steps depend on whether the sequence pattern is
   fixed or variable-length.

If the sequence pattern is fixed-length:

1. If the length of the subject sequence is not equal to the number
      of subpatterns, the sequence pattern fails

2. Subpatterns in the sequence pattern are matched to their
      corresponding items in the subject sequence from left to right.
      Matching stops as soon as a subpattern fails.  If all
      subpatterns succeed in matching their corresponding item, the
      sequence pattern succeeds.

Otherwise, if the sequence pattern is variable-length:

1. If the length of the subject sequence is less than the number of
      non-star subpatterns, the sequence pattern fails.

2. The leading non-star subpatterns are matched to their
      corresponding items as for fixed-length sequences.

3. If the previous step succeeds, the star subpattern matches a
      list formed of the remaining subject items, excluding the
      remaining items corresponding to non-star subpatterns following
      the star subpattern.

4. Remaining non-star subpatterns are matched to their
      corresponding subject items, as for a fixed-length sequence.

Note:

The length of the subject sequence is obtained via "len()" (i.e.
     via the "__len__()" protocol). This length may be cached by the
     interpreter in a similar manner as value patterns.

In simple terms "[P1, P2, P3," … ", P<N>]" matches only if all the
following happens:

* check "<subject>" is a sequence

* "len(subject) == <N>"

* "P1" matches "<subject>[0]" (note that this match can also bind
  names)

* "P2" matches "<subject>[1]" (note that this match can also bind
  names)

* … and so on for the corresponding pattern/element.

Mapping Patterns
~~~~~~~~~~~~~~~~

A mapping pattern contains one or more key-value patterns.  The syntax
is similar to the construction of a dictionary. Syntax:

mapping_pattern     ::= "{" [items_pattern] "}"
   items_pattern       ::= ",".key_value_pattern+ ","?
   key_value_pattern   ::= (literal_pattern | value_pattern) ":" pattern
                           | double_star_pattern
   double_star_pattern ::= "**" capture_pattern

At most one double star pattern may be in a mapping pattern.  The
double star pattern must be the last subpattern in the mapping
pattern.

Duplicate keys in mapping patterns are disallowed. Duplicate literal
keys will raise a "SyntaxError". Two keys that otherwise have the same
value will raise a "ValueError" at runtime.

The following is the logical flow for matching a mapping pattern
against a subject value:

1. If the subject value is not a mapping [3],the mapping pattern
   fails.

2. If every key given in the mapping pattern is present in the subject
   mapping, and the pattern for each key matches the corresponding
   item of the subject mapping, the mapping pattern succeeds.

3. If duplicate keys are detected in the mapping pattern, the pattern
   is considered invalid. A "SyntaxError" is raised for duplicate
   literal values; or a "ValueError" for named keys of the same value.

Note:

Key-value pairs are matched using the two-argument form of the
  mapping subject’s "get()" method.  Matched key-value pairs must
  already be present in the mapping, and not created on-the-fly via
  "__missing__()" or "__getitem__()".

In simple terms "{KEY1: P1, KEY2: P2, ... }" matches only if all the
following happens:

* check "<subject>" is a mapping

* "KEY1 in <subject>"

* "P1" matches "<subject>[KEY1]"

* … and so on for the corresponding KEY/pattern pair.

Class Patterns
~~~~~~~~~~~~~~

A class pattern represents a class and its positional and keyword
arguments (if any).  Syntax:

class_pattern       ::= name_or_attr "(" [pattern_arguments ","?] ")"
   pattern_arguments   ::= positional_patterns ["," keyword_patterns]
                           | keyword_patterns
   positional_patterns ::= ",".pattern+
   keyword_patterns    ::= ",".keyword_pattern+
   keyword_pattern     ::= NAME "=" pattern

The same keyword should not be repeated in class patterns.

The following is the logical flow for matching a class pattern against
a subject value:

1. If "name_or_attr" is not an instance of the builtin "type" , raise
   "TypeError".

2. If the subject value is not an instance of "name_or_attr" (tested
   via "isinstance()"), the class pattern fails.

3. If no pattern arguments are present, the pattern succeeds.
   Otherwise, the subsequent steps depend on whether keyword or
   positional argument patterns are present.

For a number of built-in types (specified below), a single
   positional subpattern is accepted which will match the entire
   subject; for these types keyword patterns also work as for other
   types.

If only keyword patterns are present, they are processed as
   follows, one by one:

1. The keyword is looked up as an attribute on the subject.

* If this raises an exception other than "AttributeError", the
        exception bubbles up.

* If this raises "AttributeError", the class pattern has failed.

* Else, the subpattern associated with the keyword pattern is
        matched against the subject’s attribute value.  If this fails,
        the class pattern fails; if this succeeds, the match proceeds
        to the next keyword.

2. If all keyword patterns succeed, the class pattern succeeds.

If any positional patterns are present, they are converted to
   keyword patterns using the "__match_args__" attribute on the class
   "name_or_attr" before matching:

1. The equivalent of "getattr(cls, "__match_args__", ())" is
      called.

* If this raises an exception, the exception bubbles up.

* If the returned value is not a tuple, the conversion fails and
        "TypeError" is raised.

* If there are more positional patterns than
        "len(cls.__match_args__)", "TypeError" is raised.

* Otherwise, positional pattern "i" is converted to a keyword
        pattern using "__match_args__[i]" as the keyword.
        "__match_args__[i]" must be a string; if not "TypeError" is
        raised.

* If there are duplicate keywords, "TypeError" is raised.

See also:

Customizing positional arguments in class pattern matching

2. Once all positional patterns have been converted to keyword
      patterns, the match proceeds as if there were only keyword
      patterns.

For the following built-in types the handling of positional
   subpatterns is different:

* "bool"

* "bytearray"

* "bytes"

* "dict"

* "float"

* "frozenset"

* "int"

* "list"

* "set"

* "str"

* "tuple"

These classes accept a single positional argument, and the pattern
   there is matched against the whole object rather than an attribute.
   For example "int(0|1)" matches the value "0", but not the value
   "0.0".

In simple terms "CLS(P1, attr=P2)" matches only if the following
happens:

* "isinstance(<subject>, CLS)"

* convert "P1" to a keyword pattern using "CLS.__match_args__"

* For each keyword argument "attr=P2":

* "hasattr(<subject>, "attr")"

* "P2" matches "<subject>.attr"

* … and so on for the corresponding keyword argument/pattern pair.

See also:

* **PEP 634** – Structural Pattern Matching: Specification

* **PEP 636** – Structural Pattern Matching: Tutorial

Function definitions
====================

A function definition defines a user-defined function object (see
section The standard type hierarchy):

funcdef                   ::= [decorators] "def" funcname [type_params] "(" [parameter_list] ")"
                                 ["->" expression] ":" suite
   decorators                ::= decorator+
   decorator                 ::= "@" assignment_expression NEWLINE
   parameter_list            ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
                                 | parameter_list_no_posonly
   parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
                                 | parameter_list_starargs
   parameter_list_starargs   ::= "*" [star_parameter] ("," defparameter)* ["," [parameter_star_kwargs]]
                                 | "*" ("," defparameter)+ ["," [parameter_star_kwargs]]
                                 | parameter_star_kwargs
   parameter_star_kwargs     ::= "**" parameter [","]
   parameter                 ::= identifier [":" expression]
   star_parameter            ::= identifier [":" ["*"] expression]
   defparameter              ::= parameter ["=" expression]
   funcname                  ::= identifier

A function definition is an executable statement.  Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function).  This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.

The function definition does not execute the function body; this gets
executed only when the function is called. [4]

A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition.  The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object.  Multiple decorators are applied in
nested fashion. For example, the following code

@f1(arg)
   @f2
   def func(): pass

is roughly equivalent to

def func(): pass
   func = f1(arg)(f2(func))

except that the original function is not temporarily bound to the name
"func".

Changed in version 3.9: Functions may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.

A list of type parameters may be given in square brackets between the
function’s name and the opening parenthesis for its parameter list.
This indicates to static type checkers that the function is generic.
At runtime, the type parameters can be retrieved from the function’s
"__type_params__" attribute. See Generic functions for more.

Changed in version 3.12: Type parameter lists are new in Python 3.12.

When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted.  If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.

**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call.  This is especially important
to understand when a default parameter value is a mutable object, such
as a list or a dictionary: if the function modifies the object (e.g.
by appending an item to a list), the default parameter value is in
effect modified.  This is generally not what was intended.  A way
around this is to use "None" as the default, and explicitly test for
it in the body of the function, e.g.:

def whats_on_the_telly(penguin=None):
       if penguin is None:
           penguin = []
       penguin.append("property of the zoo")
       return penguin

Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from positional arguments, from keyword
arguments, or from default values.  If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple. If the form
“"**identifier"” is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type.  Parameters after “"*"” or
“"*identifier"” are keyword-only parameters and may only be passed by
keyword arguments.  Parameters before “"/"” are positional-only
parameters and may only be passed by positional arguments.

Changed in version 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.

Parameters may have an *annotation* of the form “": expression"”
following the parameter name.  Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier". (As a special
case, parameters of the form "*identifier" may have an annotation “":
*expression"”.) Functions may have “return” annotation of the form
“"-> expression"” after the parameter list.  These annotations can be
any valid Python expression.  The presence of annotations does not
change the semantics of a function.  The annotation values are
available as values of a dictionary keyed by the parameters’ names in
the "__annotations__" attribute of the function object.  If the
"annotations" import from "__future__" is used, annotations are
preserved as strings at runtime which enables postponed evaluation.
Otherwise, they are evaluated when the function definition is
executed.  In this case annotations may be evaluated in a different
order than they appear in the source code.

Changed in version 3.11: Parameters of the form “"*identifier"” may
have an annotation “": *expression"”. See **PEP 646**.

It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions.  This uses lambda
expressions, described in section Lambdas.  Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression.  The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.

**Programmer’s note:** Functions are first-class objects.  A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around.  Free variables used
in the nested function can access the local variables of the function
containing the def.  See section Naming and binding for details.

See also:

**PEP 3107** - Function Annotations
     The original specification for function annotations.

**PEP 484** - Type Hints
     Definition of a standard meaning for annotations: type hints.

**PEP 526** - Syntax for Variable Annotations
     Ability to type hint variable declarations, including class
     variables and instance variables.

**PEP 563** - Postponed Evaluation of Annotations
     Support for forward references within annotations by preserving
     annotations in a string form at runtime instead of eager
     evaluation.

**PEP 318** - Decorators for Functions and Methods
     Function and method decorators were introduced. Class decorators
     were introduced in **PEP 3129**.

Class definitions
=================

A class definition defines a class object (see section The standard
type hierarchy):

classdef    ::= [decorators] "class" classname [type_params] [inheritance] ":" suite
   inheritance ::= "(" [argument_list] ")"
   classname   ::= identifier

A class definition is an executable statement.  The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing.  Classes without an inheritance list
inherit, by default, from the base class "object"; hence,

class Foo:
       pass

is equivalent to

class Foo(object):
       pass

The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.)  When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [5] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.

The order in which attributes are defined in the class body is
preserved in the new class’s "__dict__".  Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.

Class creation can be customized heavily using metaclasses.

Classes can also be decorated: just like when decorating functions,

@f1(arg)
   @f2
   class Foo: pass

is roughly equivalent to

class Foo: pass
   Foo = f1(arg)(f2(Foo))

The evaluation rules for the decorator expressions are the same as for
function decorators.  The result is then bound to the class name.

Changed in version 3.9: Classes may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.

A list of type parameters may be given in square brackets immediately
after the class’s name. This indicates to static type checkers that
the class is generic. At runtime, the type parameters can be retrieved
from the class’s "__type_params__" attribute. See Generic classes for
more.

Changed in version 3.12: Type parameter lists are new in Python 3.12.

**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances.  Instance attributes
can be set in a method with "self.name = value".  Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way.  Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results.  Descriptors can be used to create instance
variables with different implementation details.

See also:

**PEP 3115** - Metaclasses in Python 3000
     The proposal that changed the declaration of metaclasses to the
     current syntax, and the semantics for how classes with
     metaclasses are constructed.

**PEP 3129** - Class Decorators
     The proposal that added class decorators.  Function and method
     decorators were introduced in **PEP 318**.

Coroutines
==========

Added in version 3.5.

Coroutine function definition
-----------------------------

async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
                     ["->" expression] ":" suite

Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). "await" expressions, "async for" and "async
with" can only be used in the body of a coroutine function.

Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.

It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.

An example of a coroutine function:

async def func(param1, param2):
       do_stuff()
       await some_coroutine()

Changed in version 3.7: "await" and "async" are now keywords;
previously they were only treated as such inside the body of a
coroutine function.

The "async for" statement
-------------------------

async_for_stmt ::= "async" for_stmt

An *asynchronous iterable* provides an "__aiter__" method that
directly returns an *asynchronous iterator*, which can call
asynchronous code in its "__anext__" method.

The "async for" statement allows convenient iteration over
asynchronous iterables.

The following code:

async for TARGET in ITER:
       SUITE
   else:
       SUITE2

Is semantically equivalent to:

iter = (ITER).__aiter__()
   running = True

while running:
       try:
           TARGET = await iter.__anext__()
       except StopAsyncIteration:
           running = False
       else:
           SUITE
   else:
       SUITE2

except that implicit special method lookup is used for "__aiter__()"
and "__anext__()".

It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.

The "async with" statement
--------------------------

async_with_stmt ::= "async" with_stmt

An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.

The following code:

async with EXPRESSION as TARGET:
       SUITE

is semantically equivalent to:

manager = (EXPRESSION)
   aenter = manager.__aenter__
   aexit = manager.__aexit__
   value = await aenter()
   hit_except = False

try:
       TARGET = value
       SUITE
   except:
       hit_except = True
       if not await aexit(*sys.exc_info()):
           raise
   finally:
       if not hit_except:
           await aexit(None, None, None)

except that implicit special method lookup is used for "__aenter__()"
and "__aexit__()".

It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.

See also:

**PEP 492** - Coroutines with async and await syntax
     The proposal that made coroutines a proper standalone concept in
     Python, and added supporting syntax.

Type parameter lists
====================

Added in version 3.12.

Changed in version 3.13: Support for default values was added (see
**PEP 696**).

type_params  ::= "[" type_param ("," type_param)* "]"
   type_param   ::= typevar | typevartuple | paramspec
   typevar      ::= identifier (":" expression)? ("=" expression)?
   typevartuple ::= "*" identifier ("=" expression)?
   paramspec    ::= "**" identifier ("=" expression)?

Functions (including coroutines), classes and type aliases may contain
a type parameter list:

def max[T](args: list[T]) -> T:
       ...

async def amax[T](args: list[T]) -> T:
       ...

class Bag[T]:
       def __iter__(self) -> Iterator[T]:
           ...

def add(self, arg: T) -> None:
           ...

type ListOrSet[T] = list[T] | set[T]

Semantically, this indicates that the function, class, or type alias
is generic over a type variable. This information is primarily used by
static type checkers, and at runtime, generic objects behave much like
their non-generic counterparts.

Type parameters are declared in square brackets ("[]") immediately
after the name of the function, class, or type alias. The type
parameters are accessible within the scope of the generic object, but
not elsewhere. Thus, after a declaration "def func[T](): pass", the
name "T" is not available in the module scope. Below, the semantics of
generic objects are described with more precision. The scope of type
parameters is modeled with a special function (technically, an
annotation scope) that wraps the creation of the generic object.

Generic functions, classes, and type aliases have a "__type_params__"
attribute listing their type parameters.

Type parameters come in three kinds:

* "typing.TypeVar", introduced by a plain name (e.g., "T").
  Semantically, this represents a single type to a type checker.

* "typing.TypeVarTuple", introduced by a name prefixed with a single
  asterisk (e.g., "*Ts"). Semantically, this stands for a tuple of any
  number of types.

* "typing.ParamSpec", introduced by a name prefixed with two asterisks
  (e.g., "**P"). Semantically, this stands for the parameters of a
  callable.

"typing.TypeVar" declarations can define *bounds* and *constraints*
with a colon (":") followed by an expression. A single expression
after the colon indicates a bound (e.g. "T: int"). Semantically, this
means that the "typing.TypeVar" can only represent types that are a
subtype of this bound. A parenthesized tuple of expressions after the
colon indicates a set of constraints (e.g. "T: (str, bytes)"). Each
member of the tuple should be a type (again, this is not enforced at
runtime). Constrained type variables can only take on one of the types
in the list of constraints.

For "typing.TypeVar"s declared using the type parameter list syntax,
the bound and constraints are not evaluated when the generic object is
created, but only when the value is explicitly accessed through the
attributes "__bound__" and "__constraints__". To accomplish this, the
bounds or constraints are evaluated in a separate annotation scope.

"typing.TypeVarTuple"s and "typing.ParamSpec"s cannot have bounds or
constraints.

All three flavors of type parameters can also have a *default value*,
which is used when the type parameter is not explicitly provided. This
is added by appending a single equals sign ("=") followed by an
expression. Like the bounds and constraints of type variables, the
default value is not evaluated when the object is created, but only
when the type parameter’s "__default__" attribute is accessed. To this
end, the default value is evaluated in a separate annotation scope. If
no default value is specified for a type parameter, the "__default__"
attribute is set to the special sentinel object "typing.NoDefault".

The following example indicates the full set of allowed type parameter
declarations:

def overly_generic[
      SimpleTypeVar,
      TypeVarWithDefault = int,
      TypeVarWithBound: int,
      TypeVarWithConstraints: (str, bytes),
      *SimpleTypeVarTuple = (int, float),
      **SimpleParamSpec = (str, bytearray),
   ](
      a: SimpleTypeVar,
      b: TypeVarWithDefault,
      c: TypeVarWithBound,
      d: Callable[SimpleParamSpec, TypeVarWithConstraints],
      *e: SimpleTypeVarTuple,
   ): ...

Generic functions
-----------------

Generic functions are declared as follows:

def func[T](arg: T): ...

This syntax is equivalent to:

annotation-def TYPE_PARAMS_OF_func():
       T = typing.TypeVar("T")
       def func(arg: T): ...
       func.__type_params__ = (T,)
       return func
   func = TYPE_PARAMS_OF_func()

Here "annotation-def" indicates an annotation scope, which is not
actually bound to any name at runtime. (One other liberty is taken in
the translation: the syntax does not go through attribute access on
the "typing" module, but creates an instance of "typing.TypeVar"
directly.)

The annotations of generic functions are evaluated within the
annotation scope used for declaring the type parameters, but the
function’s defaults and decorators are not.

The following example illustrates the scoping rules for these cases,
as well as for additional flavors of type parameters:

@decorator
   def func[T: int, *Ts, **P](*args: *Ts, arg: Callable[P, T] = some_default):
       ...

Except for the lazy evaluation of the "TypeVar" bound, this is
equivalent to:

DEFAULT_OF_arg = some_default

annotation-def TYPE_PARAMS_OF_func():

annotation-def BOUND_OF_T():
           return int
       # In reality, BOUND_OF_T() is evaluated only on demand.
       T = typing.TypeVar("T", bound=BOUND_OF_T())

Ts = typing.TypeVarTuple("Ts")
       P = typing.ParamSpec("P")

def func(*args: *Ts, arg: Callable[P, T] = DEFAULT_OF_arg):
           ...

func.__type_params__ = (T, Ts, P)
       return func
   func = decorator(TYPE_PARAMS_OF_func())

The capitalized names like "DEFAULT_OF_arg" are not actually bound at
runtime.

Generic classes
---------------

Generic classes are declared as follows:

class Bag[T]: ...

This syntax is equivalent to:

annotation-def TYPE_PARAMS_OF_Bag():
       T = typing.TypeVar("T")
       class Bag(typing.Generic[T]):
           __type_params__ = (T,)
           ...
       return Bag
   Bag = TYPE_PARAMS_OF_Bag()

Here again "annotation-def" (not a real keyword) indicates an
annotation scope, and the name "TYPE_PARAMS_OF_Bag" is not actually
bound at runtime.

Generic classes implicitly inherit from "typing.Generic". The base
classes and keyword arguments of generic classes are evaluated within
the type scope for the type parameters, and decorators are evaluated
outside that scope. This is illustrated by this example:

@decorator
   class Bag(Base[T], arg=T): ...

This is equivalent to:

annotation-def TYPE_PARAMS_OF_Bag():
       T = typing.TypeVar("T")
       class Bag(Base[T], typing.Generic[T], arg=T):
           __type_params__ = (T,)
           ...
       return Bag
   Bag = decorator(TYPE_PARAMS_OF_Bag())

Generic type aliases
--------------------

The "type" statement can also be used to create a generic type alias:

type ListOrSet[T] = list[T] | set[T]

Except for the lazy evaluation of the value, this is equivalent to:

annotation-def TYPE_PARAMS_OF_ListOrSet():
       T = typing.TypeVar("T")

annotation-def VALUE_OF_ListOrSet():
           return list[T] | set[T]
       # In reality, the value is lazily evaluated
       return typing.TypeAliasType("ListOrSet", VALUE_OF_ListOrSet(), type_params=(T,))
   ListOrSet = TYPE_PARAMS_OF_ListOrSet()

Here, "annotation-def" (not a real keyword) indicates an annotation
scope. The capitalized names like "TYPE_PARAMS_OF_ListOrSet" are not
actually bound at runtime.

-[ Footnotes ]-

[1] The exception is propagated to the invocation stack unless there
    is a "finally" clause which happens to raise another exception.
    That new exception causes the old one to be lost.

[2] In pattern matching, a sequence is defined as one of the
    following:

* a class that inherits from "collections.abc.Sequence"

* a Python class that has been registered as
      "collections.abc.Sequence"

* a builtin class that has its (CPython) "Py_TPFLAGS_SEQUENCE" bit
      set

* a class that inherits from any of the above

The following standard library classes are sequences:

* "array.array"

* "collections.deque"

* "list"

* "memoryview"

* "range"

* "tuple"

Note:

Subject values of type "str", "bytes", and "bytearray" do not
      match sequence patterns.

[3] In pattern matching, a mapping is defined as one of the following:

* a class that inherits from "collections.abc.Mapping"

* a Python class that has been registered as
      "collections.abc.Mapping"

* a builtin class that has its (CPython) "Py_TPFLAGS_MAPPING" bit
      set

* a class that inherits from any of the above

The standard library classes "dict" and "types.MappingProxyType"
    are mappings.

[4] A string literal appearing as the first statement in the function
    body is transformed into the function’s "__doc__" attribute and
    therefore the function’s *docstring*.

[5] A string literal appearing as the first statement in the class
    body is transformed into the namespace’s "__doc__" item and
    therefore the class’s *docstring*.
zcontext-managersu���With Statement Context Managers
*******************************

A *context manager* is an object that defines the runtime context to
be established when executing a "with" statement. The context manager
handles the entry into, and the exit from, the desired runtime context
for the execution of the block of code.  Context managers are normally
invoked using the "with" statement (described in section The with
statement), but can also be used by directly invoking their methods.

Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.

For more information on context managers, see Context Manager Types.
The "object" class itself does not provide the context manager
methods.

object.__enter__(self)

Enter the runtime context related to this object. The "with"
   statement will bind this method’s return value to the target(s)
   specified in the "as" clause of the statement, if any.

object.__exit__(self, exc_type, exc_value, traceback)

Exit the runtime context related to this object. The parameters
   describe the exception that caused the context to be exited. If the
   context was exited without an exception, all three arguments will
   be "None".

If an exception is supplied, and the method wishes to suppress the
   exception (i.e., prevent it from being propagated), it should
   return a true value. Otherwise, the exception will be processed
   normally upon exit from this method.

Note that "__exit__()" methods should not reraise the passed-in
   exception; this is the caller’s responsibility.

See also:

**PEP 343** - The “with” statement
     The specification, background, and examples for the Python "with"
     statement.
�continuea���The "continue" statement
************************

continue_stmt ::= "continue"

"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.  It continues with the next cycle of the nearest enclosing loop.

When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
�conversionsu���Arithmetic conversions
**********************

When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type”, this means
that the operator implementation for built-in types works as follows:

* If either argument is a complex number, the other is converted to
  complex;

* otherwise, if either argument is a floating-point number, the other
  is converted to floating point;

* otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator).  Extensions must define their own
conversion behavior.
�
customizationu'9��Basic customization
*******************

object.__new__(cls[, ...])

Called to create a new instance of class *cls*.  "__new__()" is a
   static method (special-cased so you need not declare it as such)
   that takes the class of which an instance was requested as its
   first argument.  The remaining arguments are those passed to the
   object constructor expression (the call to the class).  The return
   value of "__new__()" should be the new object instance (usually an
   instance of *cls*).

Typical implementations create a new instance of the class by
   invoking the superclass’s "__new__()" method using
   "super().__new__(cls[, ...])" with appropriate arguments and then
   modifying the newly created instance as necessary before returning
   it.

If "__new__()" is invoked during object construction and it returns
   an instance of *cls*, then the new instance’s "__init__()" method
   will be invoked like "__init__(self[, ...])", where *self* is the
   new instance and the remaining arguments are the same as were
   passed to the object constructor.

If "__new__()" does not return an instance of *cls*, then the new
   instance’s "__init__()" method will not be invoked.

"__new__()" is intended mainly to allow subclasses of immutable
   types (like int, str, or tuple) to customize instance creation.  It
   is also commonly overridden in custom metaclasses in order to
   customize class creation.

object.__init__(self[, ...])

Called after the instance has been created (by "__new__()"), but
   before it is returned to the caller.  The arguments are those
   passed to the class constructor expression.  If a base class has an
   "__init__()" method, the derived class’s "__init__()" method, if
   any, must explicitly call it to ensure proper initialization of the
   base class part of the instance; for example:
   "super().__init__([args...])".

Because "__new__()" and "__init__()" work together in constructing
   objects ("__new__()" to create it, and "__init__()" to customize
   it), no non-"None" value may be returned by "__init__()"; doing so
   will cause a "TypeError" to be raised at runtime.

object.__del__(self)

Called when the instance is about to be destroyed.  This is also
   called a finalizer or (improperly) a destructor.  If a base class
   has a "__del__()" method, the derived class’s "__del__()" method,
   if any, must explicitly call it to ensure proper deletion of the
   base class part of the instance.

It is possible (though not recommended!) for the "__del__()" method
   to postpone destruction of the instance by creating a new reference
   to it.  This is called object *resurrection*.  It is
   implementation-dependent whether "__del__()" is called a second
   time when a resurrected object is about to be destroyed; the
   current *CPython* implementation only calls it once.

It is not guaranteed that "__del__()" methods are called for
   objects that still exist when the interpreter exits.
   "weakref.finalize" provides a straightforward way to register a
   cleanup function to be called when an object is garbage collected.

Note:

"del x" doesn’t directly call "x.__del__()" — the former
     decrements the reference count for "x" by one, and the latter is
     only called when "x"’s reference count reaches zero.

**CPython implementation detail:** It is possible for a reference
   cycle to prevent the reference count of an object from going to
   zero.  In this case, the cycle will be later detected and deleted
   by the *cyclic garbage collector*.  A common cause of reference
   cycles is when an exception has been caught in a local variable.
   The frame’s locals then reference the exception, which references
   its own traceback, which references the locals of all frames caught
   in the traceback.

See also: Documentation for the "gc" module.

Warning:

Due to the precarious circumstances under which "__del__()"
     methods are invoked, exceptions that occur during their execution
     are ignored, and a warning is printed to "sys.stderr" instead.
     In particular:

* "__del__()" can be invoked when arbitrary code is being
       executed, including from any arbitrary thread.  If "__del__()"
       needs to take a lock or invoke any other blocking resource, it
       may deadlock as the resource may already be taken by the code
       that gets interrupted to execute "__del__()".

* "__del__()" can be executed during interpreter shutdown.  As a
       consequence, the global variables it needs to access (including
       other modules) may already have been deleted or set to "None".
       Python guarantees that globals whose name begins with a single
       underscore are deleted from their module before other globals
       are deleted; if no other references to such globals exist, this
       may help in assuring that imported modules are still available
       at the time when the "__del__()" method is called.

object.__repr__(self)

Called by the "repr()" built-in function to compute the “official”
   string representation of an object.  If at all possible, this
   should look like a valid Python expression that could be used to
   recreate an object with the same value (given an appropriate
   environment).  If this is not possible, a string of the form
   "<...some useful description...>" should be returned. The return
   value must be a string object. If a class defines "__repr__()" but
   not "__str__()", then "__repr__()" is also used when an “informal”
   string representation of instances of that class is required.

This is typically used for debugging, so it is important that the
   representation is information-rich and unambiguous. A default
   implementation is provided by the "object" class itself.

object.__str__(self)

Called by "str(object)", the default "__format__()" implementation,
   and the built-in function "print()", to compute the “informal” or
   nicely printable string representation of an object.  The return
   value must be a str object.

This method differs from "object.__repr__()" in that there is no
   expectation that "__str__()" return a valid Python expression: a
   more convenient or concise representation can be used.

The default implementation defined by the built-in type "object"
   calls "object.__repr__()".

object.__bytes__(self)

Called by bytes to compute a byte-string representation of an
   object. This should return a "bytes" object. The "object" class
   itself does not provide this method.

object.__format__(self, format_spec)

Called by the "format()" built-in function, and by extension,
   evaluation of formatted string literals and the "str.format()"
   method, to produce a “formatted” string representation of an
   object. The *format_spec* argument is a string that contains a
   description of the formatting options desired. The interpretation
   of the *format_spec* argument is up to the type implementing
   "__format__()", however most classes will either delegate
   formatting to one of the built-in types, or use a similar
   formatting option syntax.

See Format specification mini-language for a description of the
   standard formatting syntax.

The return value must be a string object.

The default implementation by the "object" class should be given an
   empty *format_spec* string. It delegates to "__str__()".

Changed in version 3.4: The __format__ method of "object" itself
   raises a "TypeError" if passed any non-empty string.

Changed in version 3.7: "object.__format__(x, '')" is now
   equivalent to "str(x)" rather than "format(str(x), '')".

object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)

These are the so-called “rich comparison” methods. The
   correspondence between operator symbols and method names is as
   follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
   "x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
   "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".

A rich comparison method may return the singleton "NotImplemented"
   if it does not implement the operation for a given pair of
   arguments. By convention, "False" and "True" are returned for a
   successful comparison. However, these methods can return any value,
   so if the comparison operator is used in a Boolean context (e.g.,
   in the condition of an "if" statement), Python will call "bool()"
   on the value to determine if the result is true or false.

By default, "object" implements "__eq__()" by using "is", returning
   "NotImplemented" in the case of a false comparison: "True if x is y
   else NotImplemented". For "__ne__()", by default it delegates to
   "__eq__()" and inverts the result unless it is "NotImplemented".
   There are no other implied relationships among the comparison
   operators or default implementations; for example, the truth of
   "(x<y or x==y)" does not imply "x<=y". To automatically generate
   ordering operations from a single root operation, see
   "functools.total_ordering()".

By default, the "object" class provides implementations consistent
   with Value comparisons: equality compares according to object
   identity, and order comparisons raise "TypeError". Each default
   method may generate these results directly, but may also return
   "NotImplemented".

See the paragraph on "__hash__()" for some important notes on
   creating *hashable* objects which support custom comparison
   operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used
   when the left argument does not support the operation but the right
   argument does); rather, "__lt__()" and "__gt__()" are each other’s
   reflection, "__le__()" and "__ge__()" are each other’s reflection,
   and "__eq__()" and "__ne__()" are their own reflection. If the
   operands are of different types, and the right operand’s type is a
   direct or indirect subclass of the left operand’s type, the
   reflected method of the right operand has priority, otherwise the
   left operand’s method has priority.  Virtual subclassing is not
   considered.

When no appropriate method returns any value other than
   "NotImplemented", the "==" and "!=" operators will fall back to
   "is" and "is not", respectively.

object.__hash__(self)

Called by built-in function "hash()" and for operations on members
   of hashed collections including "set", "frozenset", and "dict".
   The "__hash__()" method should return an integer. The only required
   property is that objects which compare equal have the same hash
   value; it is advised to mix together the hash values of the
   components of the object that also play a part in comparison of
   objects by packing them into a tuple and hashing the tuple.
   Example:

def __hash__(self):
          return hash((self.name, self.nick, self.color))

Note:

"hash()" truncates the value returned from an object’s custom
     "__hash__()" method to the size of a "Py_ssize_t".  This is
     typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds.
     If an object’s   "__hash__()" must interoperate on builds of
     different bit sizes, be sure to check the width on all supported
     builds.  An easy way to do this is with "python -c "import sys;
     print(sys.hash_info.width)"".

If a class does not define an "__eq__()" method it should not
   define a "__hash__()" operation either; if it defines "__eq__()"
   but not "__hash__()", its instances will not be usable as items in
   hashable collections.  If a class defines mutable objects and
   implements an "__eq__()" method, it should not implement
   "__hash__()", since the implementation of *hashable* collections
   requires that a key’s hash value is immutable (if the object’s hash
   value changes, it will be in the wrong hash bucket).

User-defined classes have "__eq__()" and "__hash__()" methods by
   default (inherited from the "object" class); with them, all objects
   compare unequal (except with themselves) and "x.__hash__()" returns
   an appropriate value such that "x == y" implies both that "x is y"
   and "hash(x) == hash(y)".

A class that overrides "__eq__()" and does not define "__hash__()"
   will have its "__hash__()" implicitly set to "None".  When the
   "__hash__()" method of a class is "None", instances of the class
   will raise an appropriate "TypeError" when a program attempts to
   retrieve their hash value, and will also be correctly identified as
   unhashable when checking "isinstance(obj,
   collections.abc.Hashable)".

If a class that overrides "__eq__()" needs to retain the
   implementation of "__hash__()" from a parent class, the interpreter
   must be told this explicitly by setting "__hash__ =
   <ParentClass>.__hash__".

If a class that does not override "__eq__()" wishes to suppress
   hash support, it should include "__hash__ = None" in the class
   definition. A class which defines its own "__hash__()" that
   explicitly raises a "TypeError" would be incorrectly identified as
   hashable by an "isinstance(obj, collections.abc.Hashable)" call.

Note:

By default, the "__hash__()" values of str and bytes objects are
     “salted” with an unpredictable random value.  Although they
     remain constant within an individual Python process, they are not
     predictable between repeated invocations of Python.This is
     intended to provide protection against a denial-of-service caused
     by carefully chosen inputs that exploit the worst case
     performance of a dict insertion, *O*(*n*^2) complexity.  See
     https://ocert.org/advisories/ocert-2011-003.html for
     details.Changing hash values affects the iteration order of sets.
     Python has never made guarantees about this ordering (and it
     typically varies between 32-bit and 64-bit builds).See also
     "PYTHONHASHSEED".

Changed in version 3.3: Hash randomization is enabled by default.

object.__bool__(self)

Called to implement truth value testing and the built-in operation
   "bool()"; should return "False" or "True".  When this method is not
   defined, "__len__()" is called, if it is defined, and the object is
   considered true if its result is nonzero.  If a class defines
   neither "__len__()" nor "__bool__()" (which is true of the "object"
   class itself), all its instances are considered true.
�debuggeru�]��"pdb" — The Python Debugger
***************************

**Source code:** Lib/pdb.py

======================================================================

The module "pdb" defines an interactive source code debugger for
Python programs.  It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame.  It also supports post-mortem debugging
and can be called under program control.

The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source.  The extension interface uses the modules "bdb" and "cmd".

See also:

Module "faulthandler"
     Used to dump Python tracebacks explicitly, on a fault, after a
     timeout, or on a user signal.

Module "traceback"
     Standard interface to extract, format and print stack traces of
     Python programs.

The typical usage to break into the debugger is to insert:

import pdb; pdb.set_trace()

Or:

breakpoint()

at the location you want to break into the debugger, and then run the
program. You can then step through the code following this statement,
and continue running without the debugger using the "continue"
command.

Changed in version 3.7: The built-in "breakpoint()", when called with
defaults, can be used instead of "import pdb; pdb.set_trace()".

def double(x):
      breakpoint()
      return x * 2
   val = 3
   print(f"{val} * 2 is {double(val)}")

The debugger’s prompt is "(Pdb)", which is the indicator that you are
in debug mode:

> ...(2)double()
   -> breakpoint()
   (Pdb) p x
   3
   (Pdb) continue
   3 * 2 is 6

Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.

Command-line interface
======================

You can also invoke "pdb" from the command line to debug other
scripts.  For example:

python -m pdb [-c command] (-m module | pyfile) [args ...]

When invoked as a module, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally.  After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program.  Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.

-c, --command <command>

To execute commands as if given in a ".pdbrc" file; see Debugger
   commands.

Changed in version 3.2: Added the "-c" option.

-m <module>

To execute modules similar to the way "python -m" does. As with a
   script, the debugger will pause execution just before the first
   line of the module.

Changed in version 3.7: Added the "-m" option.

Typical usage to execute a statement under control of the debugger is:

>>> import pdb
   >>> def f(x):
   ...     print(1 / x)
   >>> pdb.run("f(2)")
   > <string>(1)<module>()
   (Pdb) continue
   0.5
   >>>

The typical usage to inspect a crashed program is:

>>> import pdb
   >>> def f(x):
   ...     print(1 / x)
   ...
   >>> f(0)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
     File "<stdin>", line 2, in f
   ZeroDivisionError: division by zero
   >>> pdb.pm()
   > <stdin>(2)f()
   (Pdb) p x
   0
   (Pdb)

Changed in version 3.13: The implementation of **PEP 667** means that
name assignments made via "pdb" will immediately affect the active
scope, even when running inside an *optimized scope*.

The module defines the following functions; each enters the debugger
in a slightly different way:

pdb.run(statement, globals=None, locals=None)

Execute the *statement* (given as a string or a code object) under
   debugger control.  The debugger prompt appears before any code is
   executed; you can set breakpoints and type "continue", or you can
   step through the statement using "step" or "next" (all these
   commands are explained below).  The optional *globals* and *locals*
   arguments specify the environment in which the code is executed; by
   default the dictionary of the module "__main__" is used.  (See the
   explanation of the built-in "exec()" or "eval()" functions.)

pdb.runeval(expression, globals=None, locals=None)

Evaluate the *expression* (given as a string or a code object)
   under debugger control.  When "runeval()" returns, it returns the
   value of the *expression*.  Otherwise this function is similar to
   "run()".

pdb.runcall(function, *args, **kwds)

Call the *function* (a function or method object, not a string)
   with the given arguments.  When "runcall()" returns, it returns
   whatever the function call returned.  The debugger prompt appears
   as soon as the function is entered.

pdb.set_trace(*, header=None)

Enter the debugger at the calling stack frame.  This is useful to
   hard-code a breakpoint at a given point in a program, even if the
   code is not otherwise being debugged (e.g. when an assertion
   fails).  If given, *header* is printed to the console just before
   debugging begins.

Changed in version 3.7: The keyword-only argument *header*.

Changed in version 3.13: "set_trace()" will enter the debugger
   immediately, rather than on the next line of code to be executed.

pdb.post_mortem(t=None)

Enter post-mortem debugging of the given exception or traceback
   object. If no value is given, it uses the exception that is
   currently being handled, or raises "ValueError" if there isn’t one.

Changed in version 3.13: Support for exception objects was added.

pdb.pm()

Enter post-mortem debugging of the exception found in
   "sys.last_exc".

The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name.  If you want
to access further features, you have to do this yourself:

class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)

"Pdb" is the debugger class.

The *completekey*, *stdin* and *stdout* arguments are passed to the
   underlying "cmd.Cmd" class; see the description there.

The *skip* argument, if given, must be an iterable of glob-style
   module name patterns.  The debugger will not step into frames that
   originate in a module that matches one of these patterns. [1]

By default, Pdb sets a handler for the SIGINT signal (which is sent
   when the user presses "Ctrl"-"C" on the console) when you give a
   "continue" command. This allows you to break into the debugger
   again by pressing "Ctrl"-"C".  If you want Pdb not to touch the
   SIGINT handler, set *nosigint* to true.

The *readrc* argument defaults to true and controls whether Pdb
   will load .pdbrc files from the filesystem.

Example call to enable tracing with *skip*:

import pdb; pdb.Pdb(skip=['django.*']).set_trace()

Raises an auditing event "pdb.Pdb" with no arguments.

Changed in version 3.1: Added the *skip* parameter.

Changed in version 3.2: Added the *nosigint* parameter. Previously,
   a SIGINT handler was never set by Pdb.

Changed in version 3.6: The *readrc* argument.

run(statement, globals=None, locals=None)
   runeval(expression, globals=None, locals=None)
   runcall(function, *args, **kwds)
   set_trace()

See the documentation for the functions explained above.

Debugger commands
=================

The commands recognized by the debugger are listed below.  Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs).  Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").

Entering a blank line repeats the last command entered.  Exception: if
the last command was a "list" command, the next 11 lines are listed.

Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged.  Python statements can also be prefixed with an exclamation
point ("!").  This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.

Changed in version 3.13: Expressions/Statements whose prefix is a pdb
command are now correctly identified and executed.

The debugger supports aliases.  Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.

Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.)  No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string. A
workaround for strings with double semicolons is to use implicit
string concatenation "';'';'" or "";"";"".

To set a temporary global variable, use a *convenience variable*. A
*convenience variable* is a variable whose name starts with "$".  For
example, "$foo = 1" sets a global variable "$foo" which you can use in
the debugger session.  The *convenience variables* are cleared when
the program resumes execution so it’s less likely to interfere with
your program compared to using normal variables like "foo = 1".

There are three preset *convenience variables*:

* "$_frame": the current frame you are debugging

* "$_retval": the return value if the frame is returning

* "$_exception": the exception if the frame is raising an exception

Added in version 3.12: Added the *convenience variable* feature.

If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read with "'utf-8'" encoding and executed as
if it had been typed at the debugger prompt, with the exception that
empty lines and lines starting with "#" are ignored.  This is
particularly useful for aliases.  If both files exist, the one in the
home directory is read first and aliases defined there can be
overridden by the local file.

Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next".  Previously, these
commands had no effect.

Changed in version 3.11: ".pdbrc" is now read with "'utf-8'" encoding.
Previously, it was read with the system locale encoding.

h(elp) [command]

Without argument, print the list of available commands.  With a
   *command* as argument, print help about that command.  "help pdb"
   displays the full documentation (the docstring of the "pdb"
   module).  Since the *command* argument must be an identifier, "help
   exec" must be entered to get help on the "!" command.

w(here)

Print a stack trace, with the most recent frame at the bottom.  An
   arrow (">") indicates the current frame, which determines the
   context of most commands.

d(own) [count]

Move the current frame *count* (default one) levels down in the
   stack trace (to a newer frame).

u(p) [count]

Move the current frame *count* (default one) levels up in the stack
   trace (to an older frame).

b(reak) [([filename:]lineno | function) [, condition]]

With a *lineno* argument, set a break at line *lineno* in the
   current file. The line number may be prefixed with a *filename* and
   a colon, to specify a breakpoint in another file (possibly one that
   hasn’t been loaded yet).  The file is searched on "sys.path".
   Accepatable forms of *filename* are "/abspath/to/file.py",
   "relpath/file.py", "module" and "package.module".

With a *function* argument, set a break at the first executable
   statement within that function. *function* can be any expression
   that evaluates to a function in the current namespace.

If a second argument is present, it is an expression which must
   evaluate to true before the breakpoint is honored.

Without argument, list all breaks, including for each breakpoint,
   the number of times that breakpoint has been hit, the current
   ignore count, and the associated condition if any.

Each breakpoint is assigned a number to which all the other
   breakpoint commands refer.

tbreak [([filename:]lineno | function) [, condition]]

Temporary breakpoint, which is removed automatically when it is
   first hit. The arguments are the same as for "break".

cl(ear) [filename:lineno | bpnumber ...]

With a *filename:lineno* argument, clear all the breakpoints at
   this line. With a space separated list of breakpoint numbers, clear
   those breakpoints. Without argument, clear all breaks (but first
   ask confirmation).

disable bpnumber [bpnumber ...]

Disable the breakpoints given as a space separated list of
   breakpoint numbers.  Disabling a breakpoint means it cannot cause
   the program to stop execution, but unlike clearing a breakpoint, it
   remains in the list of breakpoints and can be (re-)enabled.

enable bpnumber [bpnumber ...]

Enable the breakpoints specified.

ignore bpnumber [count]

Set the ignore count for the given breakpoint number.  If *count*
   is omitted, the ignore count is set to 0.  A breakpoint becomes
   active when the ignore count is zero.  When non-zero, the *count*
   is decremented each time the breakpoint is reached and the
   breakpoint is not disabled and any associated condition evaluates
   to true.

condition bpnumber [condition]

Set a new *condition* for the breakpoint, an expression which must
   evaluate to true before the breakpoint is honored.  If *condition*
   is absent, any existing condition is removed; i.e., the breakpoint
   is made unconditional.

commands [bpnumber]

Specify a list of commands for breakpoint number *bpnumber*.  The
   commands themselves appear on the following lines.  Type a line
   containing just "end" to terminate the commands. An example:

(Pdb) commands 1
      (com) p some_variable
      (com) end
      (Pdb)

To remove all commands from a breakpoint, type "commands" and
   follow it immediately with "end"; that is, give no commands.

With no *bpnumber* argument, "commands" refers to the last
   breakpoint set.

You can use breakpoint commands to start your program up again.
   Simply use the "continue" command, or "step", or any other command
   that resumes execution.

Specifying any command resuming execution (currently "continue",
   "step", "next", "return", "jump", "quit" and their abbreviations)
   terminates the command list (as if that command was immediately
   followed by end). This is because any time you resume execution
   (even with a simple next or step), you may encounter another
   breakpoint—which could have its own command list, leading to
   ambiguities about which list to execute.

If you use the "silent" command in the command list, the usual
   message about stopping at a breakpoint is not printed.  This may be
   desirable for breakpoints that are to print a specific message and
   then continue.  If none of the other commands print anything, you
   see no sign that the breakpoint was reached.

s(tep)

Execute the current line, stop at the first possible occasion
   (either in a function that is called or on the next line in the
   current function).

n(ext)

Continue execution until the next line in the current function is
   reached or it returns.  (The difference between "next" and "step"
   is that "step" stops inside a called function, while "next"
   executes called functions at (nearly) full speed, only stopping at
   the next line in the current function.)

unt(il) [lineno]

Without argument, continue execution until the line with a number
   greater than the current one is reached.

With *lineno*, continue execution until a line with a number
   greater or equal to *lineno* is reached.  In both cases, also stop
   when the current frame returns.

Changed in version 3.2: Allow giving an explicit line number.

r(eturn)

Continue execution until the current function returns.

c(ont(inue))

Continue execution, only stop when a breakpoint is encountered.

j(ump) lineno

Set the next line that will be executed.  Only available in the
   bottom-most frame.  This lets you jump back and execute code again,
   or jump forward to skip code that you don’t want to run.

It should be noted that not all jumps are allowed – for instance it
   is not possible to jump into the middle of a "for" loop or out of a
   "finally" clause.

l(ist) [first[, last]]

List source code for the current file.  Without arguments, list 11
   lines around the current line or continue the previous listing.
   With "." as argument, list 11 lines around the current line.  With
   one argument, list 11 lines around at that line.  With two
   arguments, list the given range; if the second argument is less
   than the first, it is interpreted as a count.

The current line in the current frame is indicated by "->".  If an
   exception is being debugged, the line where the exception was
   originally raised or propagated is indicated by ">>", if it differs
   from the current line.

Changed in version 3.2: Added the ">>" marker.

ll | longlist

List all source code for the current function or frame.
   Interesting lines are marked as for "list".

Added in version 3.2.

a(rgs)

Print the arguments of the current function and their current
   values.

p expression

Evaluate *expression* in the current context and print its value.

Note:

"print()" can also be used, but is not a debugger command — this
     executes the Python "print()" function.

pp expression

Like the "p" command, except the value of *expression* is pretty-
   printed using the "pprint" module.

whatis expression

Print the type of *expression*.

source expression

Try to get source code of *expression* and display it.

Added in version 3.2.

display [expression]

Display the value of *expression* if it changed, each time
   execution stops in the current frame.

Without *expression*, list all display expressions for the current
   frame.

Note:

Display evaluates *expression* and compares to the result of the
     previous evaluation of *expression*, so when the result is
     mutable, display may not be able to pick up the changes.

Example:

lst = []
      breakpoint()
      pass
      lst.append(1)
      print(lst)

Display won’t realize "lst" has been changed because the result of
   evaluation is modified in place by "lst.append(1)" before being
   compared:

> example.py(3)<module>()
      -> pass
      (Pdb) display lst
      display lst: []
      (Pdb) n
      > example.py(4)<module>()
      -> lst.append(1)
      (Pdb) n
      > example.py(5)<module>()
      -> print(lst)
      (Pdb)

You can do some tricks with copy mechanism to make it work:

> example.py(3)<module>()
      -> pass
      (Pdb) display lst[:]
      display lst[:]: []
      (Pdb) n
      > example.py(4)<module>()
      -> lst.append(1)
      (Pdb) n
      > example.py(5)<module>()
      -> print(lst)
      display lst[:]: [1]  [old: []]
      (Pdb)

Added in version 3.2.

undisplay [expression]

Do not display *expression* anymore in the current frame.  Without
   *expression*, clear all display expressions for the current frame.

Added in version 3.2.

interact

Start an interactive interpreter (using the "code" module) in a new
   global namespace initialised from the local and global namespaces
   for the current scope. Use "exit()" or "quit()" to exit the
   interpreter and return to the debugger.

Note:

As "interact" creates a new dedicated namespace for code
     execution, assignments to variables will not affect the original
     namespaces. However, modifications to any referenced mutable
     objects will be reflected in the original namespaces as usual.

Added in version 3.2.

Changed in version 3.13: "exit()" and "quit()" can be used to exit
   the "interact" command.

Changed in version 3.13: "interact" directs its output to the
   debugger’s output channel rather than "sys.stderr".

alias [name [command]]

Create an alias called *name* that executes *command*.  The
   *command* must *not* be enclosed in quotes.  Replaceable parameters
   can be indicated by "%1", "%2", … and "%9", while "%*" is replaced
   by all the parameters. If *command* is omitted, the current alias
   for *name* is shown. If no arguments are given, all aliases are
   listed.

Aliases may be nested and can contain anything that can be legally
   typed at the pdb prompt.  Note that internal pdb commands *can* be
   overridden by aliases.  Such a command is then hidden until the
   alias is removed.  Aliasing is recursively applied to the first
   word of the command line; all other words in the line are left
   alone.

As an example, here are two useful aliases (especially when placed
   in the ".pdbrc" file):

# Print instance variables (usage "pi classInst")
      alias pi for k in %1.__dict__.keys(): print(f"%1.{k} = {%1.__dict__[k]}")
      # Print instance variables in self
      alias ps pi self

unalias name

Delete the specified alias *name*.

! statement

Execute the (one-line) *statement* in the context of the current
   stack frame. The exclamation point can be omitted unless the first
   word of the statement resembles a debugger command, e.g.:

(Pdb) ! n=42
      (Pdb)

To set a global variable, you can prefix the assignment command
   with a "global" statement on the same line, e.g.:

(Pdb) global list_options; list_options = ['-l']
      (Pdb)

run [args ...]
restart [args ...]

Restart the debugged Python program.  If *args* is supplied, it is
   split with "shlex" and the result is used as the new "sys.argv".
   History, breakpoints, actions and debugger options are preserved.
   "restart" is an alias for "run".

q(uit)

Quit from the debugger.  The program being executed is aborted.

debug code

Enter a recursive debugger that steps through *code* (which is an
   arbitrary expression or statement to be executed in the current
   environment).

retval

Print the return value for the last return of the current function.

exceptions [excnumber]

List or jump between chained exceptions.

When using "pdb.pm()"  or "Pdb.post_mortem(...)" with a chained
   exception instead of a traceback, it allows the user to move
   between the chained exceptions using "exceptions" command to list
   exceptions, and "exceptions <number>" to switch to that exception.

Example:

def out():
          try:
              middle()
          except Exception as e:
              raise ValueError("reraise middle() error") from e

def middle():
          try:
              return inner(0)
          except Exception as e:
              raise ValueError("Middle fail")

def inner(x):
          1 / x

out()

calling "pdb.pm()" will allow to move between exceptions:

> example.py(5)out()
      -> raise ValueError("reraise middle() error") from e

(Pdb) exceptions
        0 ZeroDivisionError('division by zero')
        1 ValueError('Middle fail')
      > 2 ValueError('reraise middle() error')

(Pdb) exceptions 0
      > example.py(16)inner()
      -> 1 / x

(Pdb) up
      > example.py(10)middle()
      -> return inner(0)

Added in version 3.13.

-[ Footnotes ]-

[1] Whether a frame is considered to originate in a certain module is
    determined by the "__name__" in the frame globals.
�dela���The "del" statement
*******************

del_stmt ::= "del" target_list

Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.

Deletion of a target list recursively deletes each target, from left
to right.

Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block.  Trying to delete an unbound name
raises a "NameError" exception.

Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).

Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
�dictu��Dictionary displays
*******************

A dictionary display is a possibly empty series of dict items
(key/value pairs) enclosed in curly braces:

dict_display       ::= "{" [dict_item_list | dict_comprehension] "}"
   dict_item_list     ::= dict_item ("," dict_item)* [","]
   dict_item          ::= expression ":" expression | "**" or_expr
   dict_comprehension ::= expression ":" expression comp_for

A dictionary display yields a new dictionary object.

If a comma-separated sequence of dict items is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding value.  This means that you can specify the same key
multiple times in the dict item list, and the final dictionary’s value
for that key will be the last one given.

A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*.  Each mapping item is added to the new
dictionary.  Later values replace values already set by earlier dict
items and earlier dictionary unpackings.

Added in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.

A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.

Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy.  (To summarize, the key type
should be *hashable*, which excludes all mutable objects.)  Clashes
between duplicate keys are not detected; the last value (textually
rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions,
the evaluation order of key and value was not well-defined.  In
CPython, the value was evaluated before the key.  Starting with 3.8,
the key is evaluated before the value, as proposed by **PEP 572**.
zdynamic-featuresa���Interaction with dynamic features
*********************************

Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:

i = 10
   def f():
       print(i)
   i = 42
   f()

The "eval()" and "exec()" functions do not have access to the full
environment for resolving names.  Names may be resolved in the local
and global namespaces of the caller.  Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace.  [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace.  If only one namespace is
specified, it is used for both.
�elseaX��The "if" statement
******************

The "if" statement is used for conditional execution:

if_stmt ::= "if" assignment_expression ":" suite
               ("elif" assignment_expression ":" suite)*
               ["else" ":" suite]

It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
�
exceptionsu���Exceptions
**********

Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions.  An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.

The Python interpreter raises an exception when it detects a run-time
error (such as division by zero).  A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement.  The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.

Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).

When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop.  In
either case, it prints a stack traceback, except when the exception is
"SystemExit".

Exceptions are identified by class instances.  The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a *non-virtual base class* thereof. The
instance can be received by the handler and can carry additional
information about the exceptional condition.

Note:

Exception messages are not part of the Python API.  Their contents
  may change from one version of Python to the next without warning
  and should not be relied on by code which will run under multiple
  versions of the interpreter.

See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.

-[ Footnotes ]-

[1] This limitation occurs because the code that is executed by these
    operations is not available at the time the module is compiled.
�	execmodelu&6��Execution model
***************

Structure of a program
======================

A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block.  A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block.  A script command (a
command specified on the interpreter command line with the "-c"
option) is a code block. A module run as a top level script (as module
"__main__") from the command line using a "-m" argument is also a code
block. The string argument passed to the built-in functions "eval()"
and "exec()" is a code block.

A code block is executed in an *execution frame*.  A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.

Naming and binding
==================

Binding of names
----------------

*Names* refer to objects.  Names are introduced by name binding
operations.

The following constructs bind names:

* formal parameters to functions,

* class definitions,

* function definitions,

* assignment expressions,

* targets that are identifiers if occurring in an assignment:

* "for" loop header,

* after "as" in a "with" statement, "except" clause, "except*"
    clause, or in the as-pattern in structural pattern matching,

* in a capture pattern in structural pattern matching

* "import" statements.

* "type" statements.

* type parameter lists.

The "import" statement of the form "from ... import *" binds all names
defined in the imported module, except those beginning with an
underscore. This form may only be used at the module level.

A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).

Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).

If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global".  If a name is bound at the
module level, it is a global variable.  (The variables of the module
code block are local and global.)  If a variable is used in a code
block but not defined there, it is a *free variable*.

Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.

Resolution of names
-------------------

A *scope* defines the visibility of a name within a block.  If a local
variable is defined in a block, its scope includes that block.  If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.

When a name is used in a code block, it is resolved using the nearest
enclosing scope.  The set of all such scopes visible to a code block
is called the block’s *environment*.

When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".

If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block.  This can lead to errors when a name is used within a
block before it is bound.  This rule is subtle.  Python lacks
declarations and allows name binding operations to occur anywhere
within a code block.  The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations. See the FAQ entry on UnboundLocalError for examples.

If the "global" statement occurs within a block, all uses of the names
specified in the statement refer to the bindings of those names in the
top-level namespace.  Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins".  The global namespace is searched first.  If
the names are not found there, the builtins namespace is searched
next. If the names are also not found in the builtins namespace, new
variables are created in the global namespace. The global statement
must precede all uses of the listed names.

The "global" statement has the same scope as a name binding operation
in the same block.  If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.

The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope. Type parameters cannot be
rebound with the "nonlocal" statement.

The namespace for a module is automatically created the first time a
module is imported.  The main module for a script is always called
"__main__".

Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods.
This includes comprehensions and generator expressions, but it does
not include annotation scopes, which have access to their enclosing
class scopes. This means that the following will fail:

class A:
       a = 42
       b = list(a + i for i in range(10))

However, the following will succeed:

class A:
       type Alias = Nested
       class Nested: pass

print(A.Alias.__value__)  # <type 'A.Nested'>

Annotation scopes
-----------------

Type parameter lists and "type" statements introduce *annotation
scopes*, which behave mostly like function scopes, but with some
exceptions discussed below. *Annotations* currently do not use
annotation scopes, but they are expected to use annotation scopes in
Python 3.13 when **PEP 649** is implemented.

Annotation scopes are used in the following contexts:

* Type parameter lists for generic type aliases.

* Type parameter lists for generic functions. A generic function’s
  annotations are executed within the annotation scope, but its
  defaults and decorators are not.

* Type parameter lists for generic classes. A generic class’s base
  classes and keyword arguments are executed within the annotation
  scope, but its decorators are not.

* The bounds, constraints, and default values for type parameters
  (lazily evaluated).

* The value of type aliases (lazily evaluated).

Annotation scopes differ from function scopes in the following ways:

* Annotation scopes have access to their enclosing class namespace. If
  an annotation scope is immediately within a class scope, or within
  another annotation scope that is immediately within a class scope,
  the code in the annotation scope can use names defined in the class
  scope as if it were executed directly within the class body. This
  contrasts with regular functions defined within classes, which
  cannot access names defined in the class scope.

* Expressions in annotation scopes cannot contain "yield", "yield
  from", "await", or ":=" expressions. (These expressions are allowed
  in other scopes contained within the annotation scope.)

* Names defined in annotation scopes cannot be rebound with "nonlocal"
  statements in inner scopes. This includes only type parameters, as
  no other syntactic elements that can appear within annotation scopes
  can introduce new names.

* While annotation scopes have an internal name, that name is not
  reflected in the *qualified name* of objects defined within the
  scope. Instead, the "__qualname__" of such objects is as if the
  object were defined in the enclosing scope.

Added in version 3.12: Annotation scopes were introduced in Python
3.12 as part of **PEP 695**.

Changed in version 3.13: Annotation scopes are also used for type
parameter defaults, as introduced by **PEP 696**.

Lazy evaluation
---------------

The values of type aliases created through the "type" statement are
*lazily evaluated*. The same applies to the bounds, constraints, and
default values of type variables created through the type parameter
syntax. This means that they are not evaluated when the type alias or
type variable is created. Instead, they are only evaluated when doing
so is necessary to resolve an attribute access.

Example:

>>> type Alias = 1/0
   >>> Alias.__value__
   Traceback (most recent call last):
     ...
   ZeroDivisionError: division by zero
   >>> def func[T: 1/0](): pass
   >>> T = func.__type_params__[0]
   >>> T.__bound__
   Traceback (most recent call last):
     ...
   ZeroDivisionError: division by zero

Here the exception is raised only when the "__value__" attribute of
the type alias or the "__bound__" attribute of the type variable is
accessed.

This behavior is primarily useful for references to types that have
not yet been defined when the type alias or type variable is created.
For example, lazy evaluation enables creation of mutually recursive
type aliases:

from typing import Literal

type SimpleExpr = int | Parenthesized
   type Parenthesized = tuple[Literal["("], Expr, Literal[")"]]
   type Expr = SimpleExpr | tuple[SimpleExpr, Literal["+", "-"], Expr]

Lazily evaluated values are evaluated in annotation scope, which means
that names that appear inside the lazily evaluated value are looked up
as if they were used in the immediately enclosing scope.

Added in version 3.12.

Builtins and restricted execution
---------------------------------

**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail.  Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.

The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used).  By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.

Interaction with dynamic features
---------------------------------

Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:

i = 10
   def f():
       print(i)
   i = 42
   f()

The "eval()" and "exec()" functions do not have access to the full
environment for resolving names.  Names may be resolved in the local
and global namespaces of the caller.  Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace.  [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace.  If only one namespace is
specified, it is used for both.

Exceptions
==========

Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions.  An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.

The Python interpreter raises an exception when it detects a run-time
error (such as division by zero).  A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement.  The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.

Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).

When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop.  In
either case, it prints a stack traceback, except when the exception is
"SystemExit".

Exceptions are identified by class instances.  The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a *non-virtual base class* thereof. The
instance can be received by the handler and can carry additional
information about the exceptional condition.

Note:

Exception messages are not part of the Python API.  Their contents
  may change from one version of Python to the next without warning
  and should not be relied on by code which will run under multiple
  versions of the interpreter.

See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.

-[ Footnotes ]-

[1] This limitation occurs because the code that is executed by these
    operations is not available at the time the module is compiled.
�	exprlistsu���Expression lists
****************

starred_expression       ::= ["*"] or_expr
   flexible_expression      ::= assignment_expression | starred_expression
   flexible_expression_list ::= flexible_expression ("," flexible_expression)* [","]
   starred_expression_list  ::= starred_expression ("," starred_expression)* [","]
   expression_list          ::= expression ("," expression)* [","]
   yield_list               ::= expression_list | starred_expression "," [starred_expression_list]

Except when part of a list or set display, an expression list
containing at least one comma yields a tuple.  The length of the tuple
is the number of expressions in the list.  The expressions are
evaluated from left to right.

An asterisk "*" denotes *iterable unpacking*.  Its operand must be an
*iterable*.  The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.

Added in version 3.5: Iterable unpacking in expression lists,
originally proposed by **PEP 448**.

Added in version 3.11: Any item in an expression list may be starred.
See **PEP 646**.

A trailing comma is required only to create a one-item tuple, such as
"1,"; it is optional in all other cases. A single expression without a
trailing comma doesn’t create a tuple, but rather yields the value of
that expression. (To create an empty tuple, use an empty pair of
parentheses: "()".)
�floatinga���Floating-point literals
***********************

Floating-point literals are described by the following lexical
definitions:

floatnumber   ::= pointfloat | exponentfloat
   pointfloat    ::= [digitpart] fraction | digitpart "."
   exponentfloat ::= (digitpart | pointfloat) exponent
   digitpart     ::= digit (["_"] digit)*
   fraction      ::= "." digitpart
   exponent      ::= ("e" | "E") ["+" | "-"] digitpart

Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating-point literals is
implementation-dependent.  As in integer literals, underscores are
supported for digit grouping.

Some examples of floating-point literals:

3.14    10.    .001    1e100    3.14e-10    0e0    3.14_15_93

Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
�foru%��The "for" statement
*******************

The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:

for_stmt ::= "for" target_list "in" `!starred_list` ":" suite
                ["else" ":" suite]

The "starred_list" expression is evaluated once; it should yield an
*iterable* object.  An *iterator* is created for that iterable. The
first item provided by the iterator is then assigned to the target
list using the standard rules for assignments (see Assignment
statements), and the suite is executed.  This repeats for each item
provided by the iterator.  When the iterator is exhausted, the suite
in the "else" clause, if present, is executed, and the loop
terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite.  A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.

The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:

for i in range(10):
       print(i)
       i = 5             # this will not affect the for-loop
                         # because i will be overwritten with the next
                         # index in the range

Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop.  Hint: the built-in type "range()" represents
immutable arithmetic sequences of integers. For instance, iterating
"range(3)" successively yields 0, 1, and then 2.

Changed in version 3.11: Starred elements are now allowed in the
expression list.
�
formatstringsuk��Format string syntax
********************

The "str.format()" method and the "Formatter" class share the same
syntax for format strings (although in the case of "Formatter",
subclasses can define their own format string syntax).  The syntax is
related to that of formatted string literals, but it is less
sophisticated and, in particular, does not support arbitrary
expressions.

Format strings contain “replacement fields” surrounded by curly braces
"{}". Anything that is not contained in braces is considered literal
text, which is copied unchanged to the output.  If you need to include
a brace character in the literal text, it can be escaped by doubling:
"{{" and "}}".

The grammar for a replacement field is as follows:

replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
   field_name        ::= arg_name ("." attribute_name | "[" element_index "]")*
   arg_name          ::= [identifier | digit+]
   attribute_name    ::= identifier
   element_index     ::= digit+ | index_string
   index_string      ::= <any source character except "]"> +
   conversion        ::= "r" | "s" | "a"
   format_spec       ::= format-spec:format_spec

In less formal terms, the replacement field can start with a
*field_name* that specifies the object whose value is to be formatted
and inserted into the output instead of the replacement field. The
*field_name* is optionally followed by a  *conversion* field, which is
preceded by an exclamation point "'!'", and a *format_spec*, which is
preceded by a colon "':'".  These specify a non-default format for the
replacement value.

See also the Format specification mini-language section.

The *field_name* itself begins with an *arg_name* that is either a
number or a keyword.  If it’s a number, it refers to a positional
argument, and if it’s a keyword, it refers to a named keyword
argument. An *arg_name* is treated as a number if a call to
"str.isdecimal()" on the string would return true. If the numerical
arg_names in a format string are 0, 1, 2, … in sequence, they can all
be omitted (not just some) and the numbers 0, 1, 2, … will be
automatically inserted in that order. Because *arg_name* is not quote-
delimited, it is not possible to specify arbitrary dictionary keys
(e.g., the strings "'10'" or "':-]'") within a format string. The
*arg_name* can be followed by any number of index or attribute
expressions. An expression of the form "'.name'" selects the named
attribute using "getattr()", while an expression of the form
"'[index]'" does an index lookup using "__getitem__()".

Changed in version 3.1: The positional argument specifiers can be
omitted for "str.format()", so "'{} {}'.format(a, b)" is equivalent to
"'{0} {1}'.format(a, b)".

Changed in version 3.4: The positional argument specifiers can be
omitted for "Formatter".

Some simple format string examples:

"First, thou shalt count to {0}"  # References first positional argument
   "Bring me a {}"                   # Implicitly references the first positional argument
   "From {} to {}"                   # Same as "From {0} to {1}"
   "My quest is {name}"              # References keyword argument 'name'
   "Weight in tons {0.weight}"       # 'weight' attribute of first positional arg
   "Units destroyed: {players[0]}"   # First element of keyword argument 'players'.

The *conversion* field causes a type coercion before formatting.
Normally, the job of formatting a value is done by the "__format__()"
method of the value itself.  However, in some cases it is desirable to
force a type to be formatted as a string, overriding its own
definition of formatting.  By converting the value to a string before
calling "__format__()", the normal formatting logic is bypassed.

Three conversion flags are currently supported: "'!s'" which calls
"str()" on the value, "'!r'" which calls "repr()" and "'!a'" which
calls "ascii()".

Some examples:

"Harold's a clever {0!s}"        # Calls str() on the argument first
   "Bring out the holy {name!r}"    # Calls repr() on the argument first
   "More {!a}"                      # Calls ascii() on the argument first

The *format_spec* field contains a specification of how the value
should be presented, including such details as field width, alignment,
padding, decimal precision and so on.  Each value type can define its
own “formatting mini-language” or interpretation of the *format_spec*.

Most built-in types support a common formatting mini-language, which
is described in the next section.

A *format_spec* field can also include nested replacement fields
within it. These nested replacement fields may contain a field name,
conversion flag and format specification, but deeper nesting is not
allowed.  The replacement fields within the format_spec are
substituted before the *format_spec* string is interpreted. This
allows the formatting of a value to be dynamically specified.

See the Format examples section for some examples.

Format specification mini-language
==================================

“Format specifications” are used within replacement fields contained
within a format string to define how individual values are presented
(see Format string syntax and f-strings). They can also be passed
directly to the built-in "format()" function.  Each formattable type
may define how the format specification is to be interpreted.

Most built-in types implement the following options for format
specifications, although some of the formatting options are only
supported by the numeric types.

A general convention is that an empty format specification produces
the same result as if you had called "str()" on the value. A non-empty
format specification typically modifies the result.

The general form of a *standard format specifier* is:

format_spec ::= [options][width][grouping]["." precision][type]
   options     ::= [[fill]align][sign]["z"]["#"]["0"]
   fill        ::= <any character>
   align       ::= "<" | ">" | "=" | "^"
   sign        ::= "+" | "-" | " "
   width       ::= digit+
   grouping    ::= "," | "_"
   precision   ::= digit+
   type        ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g"
                   | "G" | "n" | "o" | "s" | "x" | "X" | "%"

If a valid *align* value is specified, it can be preceded by a *fill*
character that can be any character and defaults to a space if
omitted. It is not possible to use a literal curly brace (”"{"” or
“"}"”) as the *fill* character in a formatted string literal or when
using the "str.format()" method.  However, it is possible to insert a
curly brace with a nested replacement field.  This limitation doesn’t
affect the "format()" function.

The meaning of the various alignment options is as follows:

+-----------+------------------------------------------------------------+
| Option    | Meaning                                                    |
|===========|============================================================|
| "'<'"     | Forces the field to be left-aligned within the available   |
|           | space (this is the default for most objects).              |
+-----------+------------------------------------------------------------+
| "'>'"     | Forces the field to be right-aligned within the available  |
|           | space (this is the default for numbers).                   |
+-----------+------------------------------------------------------------+
| "'='"     | Forces the padding to be placed after the sign (if any)    |
|           | but before the digits.  This is used for printing fields   |
|           | in the form ‘+000000120’. This alignment option is only    |
|           | valid for numeric types, excluding "complex". It becomes   |
|           | the default for numbers when ‘0’ immediately precedes the  |
|           | field width.                                               |
+-----------+------------------------------------------------------------+
| "'^'"     | Forces the field to be centered within the available       |
|           | space.                                                     |
+-----------+------------------------------------------------------------+

Note that unless a minimum field width is defined, the field width
will always be the same size as the data to fill it, so that the
alignment option has no meaning in this case.

The *sign* option is only valid for number types, and can be one of
the following:

+-----------+------------------------------------------------------------+
| Option    | Meaning                                                    |
|===========|============================================================|
| "'+'"     | Indicates that a sign should be used for both positive as  |
|           | well as negative numbers.                                  |
+-----------+------------------------------------------------------------+
| "'-'"     | Indicates that a sign should be used only for negative     |
|           | numbers (this is the default behavior).                    |
+-----------+------------------------------------------------------------+
| space     | Indicates that a leading space should be used on positive  |
|           | numbers, and a minus sign on negative numbers.             |
+-----------+------------------------------------------------------------+

The "'z'" option coerces negative zero floating-point values to
positive zero after rounding to the format precision.  This option is
only valid for floating-point presentation types.

Changed in version 3.11: Added the "'z'" option (see also **PEP
682**).

The "'#'" option causes the “alternate form” to be used for the
conversion.  The alternate form is defined differently for different
types.  This option is only valid for integer, float and complex
types. For integers, when binary, octal, or hexadecimal output is
used, this option adds the respective prefix "'0b'", "'0o'", "'0x'",
or "'0X'" to the output value. For float and complex the alternate
form causes the result of the conversion to always contain a decimal-
point character, even if no digits follow it. Normally, a decimal-
point character appears in the result of these conversions only if a
digit follows it. In addition, for "'g'" and "'G'" conversions,
trailing zeros are not removed from the result.

The *width* is a decimal integer defining the minimum total field
width, including any prefixes, separators, and other formatting
characters. If not specified, then the field width will be determined
by the content.

When no explicit alignment is given, preceding the *width* field by a
zero ("'0'") character enables sign-aware zero-padding for numeric
types, excluding "complex".  This is equivalent to a *fill* character
of "'0'" with an *alignment* type of "'='".

Changed in version 3.10: Preceding the *width* field by "'0'" no
longer affects the default alignment for strings.

The *grouping* option after the *width* field specifies a digit group
separator for the integral part of a number. It can be one of the
following:

+-----------+------------------------------------------------------------+
| Option    | Meaning                                                    |
|===========|============================================================|
| "','"     | Inserts a comma every 3 digits for integer presentation    |
|           | type "'d'" and floating-point presentation types,          |
|           | excluding "'n'". For other presentation types, this option |
|           | is not supported.                                          |
+-----------+------------------------------------------------------------+
| "'_'"     | Inserts an underscore every 3 digits for integer           |
|           | presentation type "'d'" and floating-point presentation    |
|           | types, excluding "'n'". For integer presentation types     |
|           | "'b'", "'o'", "'x'", and "'X'", underscores are inserted   |
|           | every 4 digits. For other presentation types, this option  |
|           | is not supported.                                          |
+-----------+------------------------------------------------------------+

For a locale aware separator, use the "'n'" presentation type instead.

Changed in version 3.1: Added the "','" option (see also **PEP 378**).

Changed in version 3.6: Added the "'_'" option (see also **PEP 515**).

The *precision* is a decimal integer indicating how many digits should
be displayed after the decimal point for presentation types "'f'" and
"'F'", or before and after the decimal point for presentation types
"'g'" or "'G'".  For string presentation types the field indicates the
maximum field size - in other words, how many characters will be used
from the field content.  The *precision* is not allowed for integer
presentation types.

Finally, the *type* determines how the data should be presented.

The available string presentation types are:

+-----------+------------------------------------------------------------+
   | Type      | Meaning                                                    |
   |===========|============================================================|
   | "'s'"     | String format. This is the default type for strings and    |
   |           | may be omitted.                                            |
   +-----------+------------------------------------------------------------+
   | None      | The same as "'s'".                                         |
   +-----------+------------------------------------------------------------+

The available integer presentation types are:

+-----------+------------------------------------------------------------+
   | Type      | Meaning                                                    |
   |===========|============================================================|
   | "'b'"     | Binary format. Outputs the number in base 2.               |
   +-----------+------------------------------------------------------------+
   | "'c'"     | Character. Converts the integer to the corresponding       |
   |           | unicode character before printing.                         |
   +-----------+------------------------------------------------------------+
   | "'d'"     | Decimal Integer. Outputs the number in base 10.            |
   +-----------+------------------------------------------------------------+
   | "'o'"     | Octal format. Outputs the number in base 8.                |
   +-----------+------------------------------------------------------------+
   | "'x'"     | Hex format. Outputs the number in base 16, using lower-    |
   |           | case letters for the digits above 9.                       |
   +-----------+------------------------------------------------------------+
   | "'X'"     | Hex format. Outputs the number in base 16, using upper-    |
   |           | case letters for the digits above 9. In case "'#'" is      |
   |           | specified, the prefix "'0x'" will be upper-cased to "'0X'" |
   |           | as well.                                                   |
   +-----------+------------------------------------------------------------+
   | "'n'"     | Number. This is the same as "'d'", except that it uses the |
   |           | current locale setting to insert the appropriate digit     |
   |           | group separators.                                          |
   +-----------+------------------------------------------------------------+
   | None      | The same as "'d'".                                         |
   +-----------+------------------------------------------------------------+

In addition to the above presentation types, integers can be formatted
with the floating-point presentation types listed below (except "'n'"
and "None"). When doing so, "float()" is used to convert the integer
to a floating-point number before formatting.

The available presentation types for "float" and "Decimal" values are:

+-----------+------------------------------------------------------------+
   | Type      | Meaning                                                    |
   |===========|============================================================|
   | "'e'"     | Scientific notation. For a given precision "p", formats    |
   |           | the number in scientific notation with the letter ‘e’      |
   |           | separating the coefficient from the exponent. The          |
   |           | coefficient has one digit before and "p" digits after the  |
   |           | decimal point, for a total of "p + 1" significant digits.  |
   |           | With no precision given, uses a precision of "6" digits    |
   |           | after the decimal point for "float", and shows all         |
   |           | coefficient digits for "Decimal".  If "p=0", the decimal   |
   |           | point is omitted unless the "#" option is used.  For       |
   |           | "float", the exponent always contains at least two digits, |
   |           | and is zero if the value is zero.                          |
   +-----------+------------------------------------------------------------+
   | "'E'"     | Scientific notation. Same as "'e'" except it uses an upper |
   |           | case ‘E’ as the separator character.                       |
   +-----------+------------------------------------------------------------+
   | "'f'"     | Fixed-point notation. For a given precision "p", formats   |
   |           | the number as a decimal number with exactly "p" digits     |
   |           | following the decimal point. With no precision given, uses |
   |           | a precision of "6" digits after the decimal point for      |
   |           | "float", and uses a precision large enough to show all     |
   |           | coefficient digits for "Decimal".  If "p=0", the decimal   |
   |           | point is omitted unless the "#" option is used.            |
   +-----------+------------------------------------------------------------+
   | "'F'"     | Fixed-point notation. Same as "'f'", but converts "nan" to |
   |           | "NAN" and "inf" to "INF".                                  |
   +-----------+------------------------------------------------------------+
   | "'g'"     | General format.  For a given precision "p >= 1", this      |
   |           | rounds the number to "p" significant digits and then       |
   |           | formats the result in either fixed-point format or in      |
   |           | scientific notation, depending on its magnitude. A         |
   |           | precision of "0" is treated as equivalent to a precision   |
   |           | of "1".  The precise rules are as follows: suppose that    |
   |           | the result formatted with presentation type "'e'" and      |
   |           | precision "p-1" would have exponent "exp".  Then, if "m <= |
   |           | exp < p", where "m" is -4 for floats and -6 for            |
   |           | "Decimals", the number is formatted with presentation type |
   |           | "'f'" and precision "p-1-exp".  Otherwise, the number is   |
   |           | formatted with presentation type "'e'" and precision       |
   |           | "p-1". In both cases insignificant trailing zeros are      |
   |           | removed from the significand, and the decimal point is     |
   |           | also removed if there are no remaining digits following    |
   |           | it, unless the "'#'" option is used.  With no precision    |
   |           | given, uses a precision of "6" significant digits for      |
   |           | "float". For "Decimal", the coefficient of the result is   |
   |           | formed from the coefficient digits of the value;           |
   |           | scientific notation is used for values smaller than "1e-6" |
   |           | in absolute value and values where the place value of the  |
   |           | least significant digit is larger than 1, and fixed-point  |
   |           | notation is used otherwise.  Positive and negative         |
   |           | infinity, positive and negative zero, and nans, are        |
   |           | formatted as "inf", "-inf", "0", "-0" and "nan"            |
   |           | respectively, regardless of the precision.                 |
   +-----------+------------------------------------------------------------+
   | "'G'"     | General format. Same as "'g'" except switches to "'E'" if  |
   |           | the number gets too large. The representations of infinity |
   |           | and NaN are uppercased, too.                               |
   +-----------+------------------------------------------------------------+
   | "'n'"     | Number. This is the same as "'g'", except that it uses the |
   |           | current locale setting to insert the appropriate digit     |
   |           | group separators for the integral part of a number.        |
   +-----------+------------------------------------------------------------+
   | "'%'"     | Percentage. Multiplies the number by 100 and displays in   |
   |           | fixed ("'f'") format, followed by a percent sign.          |
   +-----------+------------------------------------------------------------+
   | None      | For "float" this is like the "'g'" type, except that when  |
   |           | fixed- point notation is used to format the result, it     |
   |           | always includes at least one digit past the decimal point, |
   |           | and switches to the scientific notation when "exp >= p -   |
   |           | 1".  When the precision is not specified, the latter will  |
   |           | be as large as needed to represent the given value         |
   |           | faithfully.  For "Decimal", this is the same as either     |
   |           | "'g'" or "'G'" depending on the value of                   |
   |           | "context.capitals" for the current decimal context.  The   |
   |           | overall effect is to match the output of "str()" as        |
   |           | altered by the other format modifiers.                     |
   +-----------+------------------------------------------------------------+

The result should be correctly rounded to a given precision "p" of
digits after the decimal point.  The rounding mode for "float" matches
that of the "round()" builtin.  For "Decimal", the rounding mode of
the current context will be used.

The available presentation types for "complex" are the same as those
for "float" ("'%'" is not allowed).  Both the real and imaginary
components of a complex number are formatted as floating-point
numbers, according to the specified presentation type.  They are
separated by the mandatory sign of the imaginary part, the latter
being terminated by a "j" suffix.  If the presentation type is
missing, the result will match the output of "str()" (complex numbers
with a non-zero real part are also surrounded by parentheses),
possibly altered by other format modifiers.

Format examples
===============

This section contains examples of the "str.format()" syntax and
comparison with the old "%"-formatting.

In most of the cases the syntax is similar to the old "%"-formatting,
with the addition of the "{}" and with ":" used instead of "%". For
example, "'%03.2f'" can be translated to "'{:03.2f}'".

The new format syntax also supports new and different options, shown
in the following examples.

Accessing arguments by position:

>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
   'a, b, c'
   >>> '{}, {}, {}'.format('a', 'b', 'c')  # 3.1+ only
   'a, b, c'
   >>> '{2}, {1}, {0}'.format('a', 'b', 'c')
   'c, b, a'
   >>> '{2}, {1}, {0}'.format(*'abc')      # unpacking argument sequence
   'c, b, a'
   >>> '{0}{1}{0}'.format('abra', 'cad')   # arguments' indices can be repeated
   'abracadabra'

Accessing arguments by name:

>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
   'Coordinates: 37.24N, -115.81W'
   >>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
   >>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
   'Coordinates: 37.24N, -115.81W'

Accessing arguments’ attributes:

>>> c = 3-5j
   >>> ('The complex number {0} is formed from the real part {0.real} '
   ...  'and the imaginary part {0.imag}.').format(c)
   'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
   >>> class Point:
   ...     def __init__(self, x, y):
   ...         self.x, self.y = x, y
   ...     def __str__(self):
   ...         return 'Point({self.x}, {self.y})'.format(self=self)
   ...
   >>> str(Point(4, 2))
   'Point(4, 2)'

Accessing arguments’ items:

>>> coord = (3, 5)
   >>> 'X: {0[0]};  Y: {0[1]}'.format(coord)
   'X: 3;  Y: 5'

Replacing "%s" and "%r":

>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
   "repr() shows quotes: 'test1'; str() doesn't: test2"

Aligning the text and specifying a width:

>>> '{:<30}'.format('left aligned')
   'left aligned                  '
   >>> '{:>30}'.format('right aligned')
   '                 right aligned'
   >>> '{:^30}'.format('centered')
   '           centered           '
   >>> '{:*^30}'.format('centered')  # use '*' as a fill char
   '***********centered***********'

Replacing "%+f", "%-f", and "% f" and specifying a sign:

>>> '{:+f}; {:+f}'.format(3.14, -3.14)  # show it always
   '+3.140000; -3.140000'
   >>> '{: f}; {: f}'.format(3.14, -3.14)  # show a space for positive numbers
   ' 3.140000; -3.140000'
   >>> '{:-f}; {:-f}'.format(3.14, -3.14)  # show only the minus -- same as '{:f}; {:f}'
   '3.140000; -3.140000'

Replacing "%x" and "%o" and converting the value to different bases:

>>> # format also supports binary numbers
   >>> "int: {0:d};  hex: {0:x};  oct: {0:o};  bin: {0:b}".format(42)
   'int: 42;  hex: 2a;  oct: 52;  bin: 101010'
   >>> # with 0x, 0o, or 0b as prefix:
   >>> "int: {0:d};  hex: {0:#x};  oct: {0:#o};  bin: {0:#b}".format(42)
   'int: 42;  hex: 0x2a;  oct: 0o52;  bin: 0b101010'

Using the comma or the underscore as a digit group separator:

>>> '{:,}'.format(1234567890)
   '1,234,567,890'
   >>> '{:_}'.format(1234567890)
   '1_234_567_890'
   >>> '{:_b}'.format(1234567890)
   '100_1001_1001_0110_0000_0010_1101_0010'
   >>> '{:_x}'.format(1234567890)
   '4996_02d2'

Expressing a percentage:

>>> points = 19
   >>> total = 22
   >>> 'Correct answers: {:.2%}'.format(points/total)
   'Correct answers: 86.36%'

Using type-specific formatting:

>>> import datetime as dt
   >>> d = dt.datetime(2010, 7, 4, 12, 15, 58)
   >>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
   '2010-07-04 12:15:58'

Nesting arguments and more complex examples:

>>> for align, text in zip('<^>', ['left', 'center', 'right']):
   ...     '{0:{fill}{align}16}'.format(text, fill=align, align=align)
   ...
   'left<<<<<<<<<<<<'
   '^^^^^center^^^^^'
   '>>>>>>>>>>>right'
   >>>
   >>> octets = [192, 168, 0, 1]
   >>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
   'C0A80001'
   >>> int(_, 16)
   3232235521
   >>>
   >>> width = 5
   >>> for num in range(5,12):
   ...     for base in 'dXob':
   ...         print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
   ...     print()
   ...
       5     5     5   101
       6     6     6   110
       7     7     7   111
       8     8    10  1000
       9     9    11  1001
      10     A    12  1010
      11     B    13  1011
�functionu��Function definitions
********************